Hello Kiss me, shall I introduce myself?

Well, that's a new one — I appreciate the sense of humor, but let's keep things focused on the interview. If you'd like to introduce yourself, please go ahead, Professor.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic.

You mentioned your background is in logistics — can you walk me through a specific project or research topic you've worked on that connects logistics with mathematics?

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here.

Okay, so you grew up in a village and studied your undergraduate degree at a government college with a focus on logistics. How did your background in logistics lead you toward mathematics research, Professor?

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate. Finally, I completed my PhD. So after that I I. Start tu hai. Career after the PhD. Hey Cortana, opportunity to. Because he has still focused that in there. Department of Metro. Scholarship University.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate. Finally, I completed my PhD. So after that I I. Start tu hai. Career after the PhD. Hey Cortana, opportunity to. Because he has still focused that in there. Department of Metro. Scholarship University. I worked in one semester. That means, uh, almost 11 semester. I completed my. Called semester work in. That particular theory. Within the four months I got the opportunity to do. Post doc research in the. Department of Industrial Engineering and.

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate. Finally, I completed my PhD. So after that I I. Start tu hai. Career after the PhD. Hey Cortana, opportunity to. Because he has still focused that in there. Department of Metro. Scholarship University. I worked in one semester. That means, uh, almost 11 semester. I completed my. Called semester work in. That particular theory. Within the four months I got the opportunity to do. Post doc research in the. Department of Industrial Engineering and. South Korea. India University. So I. Started my. Postgres. Month of August 2022. After that I continue my only at sound length of.

Alright, Dr. Dharmalingam, you've taken quite an academic journey from your village to earning your PhD at NIT. Since you mentioned logistics as a key thread throughout, can you walk me through a research project or publication where you applied advanced mathematical methods to solve a logistics or supply chain problem?

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate. Finally, I completed my PhD. So after that I I. Start tu hai. Career after the PhD. Hey Cortana, opportunity to. Because he has still focused that in there. Department of Metro. Scholarship University. I worked in one semester. That means, uh, almost 11 semester. I completed my. Called semester work in. That particular theory. Within the four months I got the opportunity to do. Post doc research in the. Department of Industrial Engineering and. South Korea. India University. So I. Started my. Postgres. Month of August 2022. After that I continue my only at sound length of. Research work. After PhD. So the my professor said that. Work was very amazing. After that I continued. Contact, but unfortunately I. Unable to get the. So that I. Completed the contact. All right, so after that I can. Start again. Are you in India? I got the opportunity. To work as a CSP.

Alright, so you started with a focus on logistics, then moved through your master's and MPhil, and finally earned your PhD at NIT with an institute fellowship. Since your path has spanned several advanced degrees, I'm curious — during your doctoral research at NIT, what was the main problem or question you set out to solve, and how did you approach it mathematically?

Myself, Doctor Dharmalingam. My native is. Thrown up logistic. And I came from. Village Back Rd. And also I studied my undergraduation. Govt articleised Theranamelistic. After that I continue my. I got education in the. Master of Degree. Master PL. and continue my must. After the Master degree I continued my Master of Philosophy. MP. After that I completed my. Master classic studies. I got the opportunity to do that. Eye studies in the. Caprara Π. That means my PhD degree in the NIT Pulitzeri Coracle. I got an opportunity. Through the UH gate score. I clear my recommendation. And I got the admission. With the Institute fellowship. After that. Started my research here. From 2018. 2013. Around 4.5 inches. Softer than a graduate I have published. 5 parasites. Application. Very repeated. After that. PC members are recommended. Also. So after that I got that. Degree certificate. Finally, I completed my PhD. So after that I I. Start tu hai. Career after the PhD. Hey Cortana, opportunity to. Because he has still focused that in there. Department of Metro. Scholarship University. I worked in one semester. That means, uh, almost 11 semester. I completed my. Called semester work in. That particular theory. Within the four months I got the opportunity to do. Post doc research in the. Department of Industrial Engineering and. South Korea. India University. So I. Started my. Postgres. Month of August 2022. After that I continue my only at sound length of. Research work. After PhD. So the my professor said that. Work was very amazing. After that I continued. Contact, but unfortunately I. Unable to get the. So that I. Completed the contact. All right, so after that I can. Start again. Are you in India? I got the opportunity. To work as a CSP. Department. Which so now current. Is there any? Application Sir. Hello.

Alright, so you’ve moved from a village background through undergraduate, master’s, MPhil, and then a PhD at NIT, all the way with fellowships and several publications. You mentioned your research started in 2013 and you published five papers in well-regarded venues. Can you tell me about the main focus of your PhD research, and what specific problem in mathematics or logistics you addressed?

Okay, Dr. Dharmalingam, sounds like you've followed a rigorous academic path — from undergraduate in your village, through M.Phil., and then your PhD at NIT with an institute fellowship. You mentioned publishing five papers in reputable journals during your doctoral work. Can you describe one of those publications and explain what made its contribution significant to the field?

Alright, so you completed your PhD at NIT and worked for a semester in the Department of Mathematics before moving into postdoctoral research in Industrial Engineering. Can you tell me about a specific research problem you tackled during your PhD, and what original contribution you made to the field?

Okay, so you completed your PhD at NIT and then moved into postdoctoral research in South Korea after working a semester in India. You mentioned publishing five articles in reputable journals during your doctoral work — can you share which mathematical area your research focused on, and what impact you believe your work has had on that field?

Okay, so you started in a rural area, moved through a range of degrees including MPhil and PhD at NIT, and then you did a postdoc in South Korea—sounds like you've covered a lot of ground in both research and teaching environments. When you were at NIT working on your PhD, what was the central research problem you focused on, and what motivated you to choose that topic?

Alright, Dr. Dharmalingam, you mentioned your research at NIT and your postdoctoral work in South Korea — can you tell me about a specific research problem you tackled during your PhD and how you approached solving it?

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization.

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem.

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem. In that period. I solved the ***** decision making problem. That means I using their fuzzy numbers and fuzzy techniques. That means. Uh, absence technique. And the electric electric method. And also the various types of decision making tools I have. Just for the solving the decision making problem. And also I implemented. Many of the. Fuzzy numbers. That means that. Triangle Fasnagar. To solve the. Just making problem.

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem. In that period. I solved the ***** decision making problem. That means I using their fuzzy numbers and fuzzy techniques. That means. Uh, absence technique. And the electric electric method. And also the various types of decision making tools I have. Just for the solving the decision making problem. And also I implemented. Many of the. Fuzzy numbers. That means that. Triangle Fasnagar. To solve the. Just making problem. In that concept. Stopping the decision making problem is nothing but. We have the some alternatives. Under certain criteria. Based on the certain criteria, you have to choose the best alternative. Suppose we have that. N number of uh. Car company. That means different style of the company. How the alternatives that means? Some different types of car. We have to select the best car. Based on our recruitments. The requirements is nothing but. We have the number of criteria. Maybe the style? Yeah, someone like we can say, they say, may say style is good. And safety comfortable. C Presley's number of C facilities and also the POF CNC. And also the number of kilometers that will give the K number of. Cost reducing. These are the criteria basic criteria to select the best car among the several models of the car. So in that case I started to solve the problems. I have taken the criteria, alternatives and criteria. To solve the testmaking problem. So in that decision making problem I have uh. Generated the matrix. And that matrix is nothing but matters. Alternative s s. Criteria. So the alternative is. It's nothing but. Our different types of different models of car. Criteria has nothing but different. Types of recruits. What we need to. Select Select that. Sophisticated, Uh. One so that I started the matrix. That means we can say the π criteria and π versus ππ alternatives. That means Spycraft's PyTorch 5 matrix.

Okay, so you worked on decision-making and optimization problems for your research. With that background — when you're teaching advanced undergraduate students about optimization in supply chain management, how do you simplify complex mathematical models so they really grasp the core ideas?

Okay, so your research focused on decision-making problems in optimization, especially selecting the best alternative among many based on several criteria. When you were designing your method, how did you handle situations where the criteria conflicted with each other — for example, when one alternative scored high on cost but low on reliability?

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem. In that period. I solved the ***** decision making problem. That means I using their fuzzy numbers and fuzzy techniques. That means. Uh, absence technique. And the electric electric method. And also the various types of decision making tools I have. Just for the solving the decision making problem. And also I implemented. Many of the. Fuzzy numbers. That means that. Triangle Fasnagar. To solve the. Just making problem. In that concept. Stopping the decision making problem is nothing but. We have the some alternatives. Under certain criteria. Based on the certain criteria, you have to choose the best alternative. Suppose we have that. N number of uh. Car company. That means different style of the company. How the alternatives that means? Some different types of car. We have to select the best car. Based on our recruitments. The requirements is nothing but. We have the number of criteria. Maybe the style? Yeah, someone like we can say, they say, may say style is good. And safety comfortable. C Presley's number of C facilities and also the POF CNC. And also the number of kilometers that will give the K number of. Cost reducing. These are the criteria basic criteria to select the best car among the several models of the car. So in that case I started to solve the problems. I have taken the criteria, alternatives and criteria. To solve the testmaking problem. So in that decision making problem I have uh. Generated the matrix. And that matrix is nothing but matters. Alternative s s. Criteria. So the alternative is. It's nothing but. Our different types of different models of car. Criteria has nothing but different. Types of recruits. What we need to. Select Select that. Sophisticated, Uh. One so that I started the matrix. That means we can say the π criteria and π versus ππ alternatives. That means Spycraft's PyTorch 5 matrix. After that, uh, we can solve the N number of alternatives and the N number of criteria. But initially I started the. Pinegrass 5 prime matrix is. I have taken the five alternatives with the five. Suitable criteria. So this is the first. But habitat to solve by decision making problems. In that case I have entered my input is nothing but linguistic quasi variable. In that perseverable is nothing but. Using the linguistic concept that means. We have the passive, uh, linguistic terms. That means low, medium. I had extremely low on ex medium low. And also the very high, very. Extremely high like that. These are the linguistic. Thank you. Depending upon the users. They can decide the input variable. Restaurant input variable. Uh, we can, uh. Ascend uh assumed our uh. Input uh. Criterias. Agreements after that I have to. Generate the matrices using the matrixes I can. One there mattress is nothing but. Normalized matrices. Using the normalized 2 emphasis, I'm going to use the. Particular, uh.

Okay, so you focused on multi-criteria decision making using fuzzy numbers and approaches like the ELECTRE method. When you were comparing alternatives with those fuzzy techniques, how did you validate that your method actually gave better or more robust results than traditional approaches?

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem. In that period. I solved the ***** decision making problem. That means I using their fuzzy numbers and fuzzy techniques. That means. Uh, absence technique. And the electric electric method. And also the various types of decision making tools I have. Just for the solving the decision making problem. And also I implemented. Many of the. Fuzzy numbers. That means that. Triangle Fasnagar. To solve the. Just making problem. In that concept. Stopping the decision making problem is nothing but. We have the some alternatives. Under certain criteria. Based on the certain criteria, you have to choose the best alternative. Suppose we have that. N number of uh. Car company. That means different style of the company. How the alternatives that means? Some different types of car. We have to select the best car. Based on our recruitments. The requirements is nothing but. We have the number of criteria. Maybe the style? Yeah, someone like we can say, they say, may say style is good. And safety comfortable. C Presley's number of C facilities and also the POF CNC. And also the number of kilometers that will give the K number of. Cost reducing. These are the criteria basic criteria to select the best car among the several models of the car. So in that case I started to solve the problems. I have taken the criteria, alternatives and criteria. To solve the testmaking problem. So in that decision making problem I have uh. Generated the matrix. And that matrix is nothing but matters. Alternative s s. Criteria. So the alternative is. It's nothing but. Our different types of different models of car. Criteria has nothing but different. Types of recruits. What we need to. Select Select that. Sophisticated, Uh. One so that I started the matrix. That means we can say the π criteria and π versus ππ alternatives. That means Spycraft's PyTorch 5 matrix. After that, uh, we can solve the N number of alternatives and the N number of criteria. But initially I started the. Pinegrass 5 prime matrix is. I have taken the five alternatives with the five. Suitable criteria. So this is the first. But habitat to solve by decision making problems. In that case I have entered my input is nothing but linguistic quasi variable. In that perseverable is nothing but. Using the linguistic concept that means. We have the passive, uh, linguistic terms. That means low, medium. I had extremely low on ex medium low. And also the very high, very. Extremely high like that. These are the linguistic. Thank you. Depending upon the users. They can decide the input variable. Restaurant input variable. Uh, we can, uh. Ascend uh assumed our uh. Input uh. Criterias. Agreements after that I have to. Generate the matrices using the matrixes I can. One there mattress is nothing but. Normalized matrices. Using the normalized 2 emphasis, I'm going to use the. Particular, uh. Topsy's technique. I initially started that technique. That means it's not a traditional one. It is Fuzzy Thomas. Instead of uh. We are going to be implement. Fazi naman that means. Uh, why? Because why we need to use the fuzzy number means we have the, uh, situation. In the imprecise situation and also the baggage situation.

Yes, uh, it's amazing question. So in my research career. I started the. What is now? My uh, most of classify. That means I started. To solve the problem. Using using decision making. Problem in the domain of optimization. So in this case. Uh, I started the solving the addition making problem. Different. Uh, scenario. That means. Uh, criteria. Number of criteria versus number of alternatives. We have to choose their best alternatives. Using the. Certain criterias. That means, suppose we have a different type of alternatives. In that same criteria. How to select the best alternatives based on your? Research based on our. That means. I started my uh. Research problem in the concept of decision making problem. In that period. I solved the ***** decision making problem. That means I using their fuzzy numbers and fuzzy techniques. That means. Uh, absence technique. And the electric electric method. And also the various types of decision making tools I have. Just for the solving the decision making problem. And also I implemented. Many of the. Fuzzy numbers. That means that. Triangle Fasnagar. To solve the. Just making problem. In that concept. Stopping the decision making problem is nothing but. We have the some alternatives. Under certain criteria. Based on the certain criteria, you have to choose the best alternative. Suppose we have that. N number of uh. Car company. That means different style of the company. How the alternatives that means? Some different types of car. We have to select the best car. Based on our recruitments. The requirements is nothing but. We have the number of criteria. Maybe the style? Yeah, someone like we can say, they say, may say style is good. And safety comfortable. C Presley's number of C facilities and also the POF CNC. And also the number of kilometers that will give the K number of. Cost reducing. These are the criteria basic criteria to select the best car among the several models of the car. So in that case I started to solve the problems. I have taken the criteria, alternatives and criteria. To solve the testmaking problem. So in that decision making problem I have uh. Generated the matrix. And that matrix is nothing but matters. Alternative s s. Criteria. So the alternative is. It's nothing but. Our different types of different models of car. Criteria has nothing but different. Types of recruits. What we need to. Select Select that. Sophisticated, Uh. One so that I started the matrix. That means we can say the π criteria and π versus ππ alternatives. That means Spycraft's PyTorch 5 matrix. After that, uh, we can solve the N number of alternatives and the N number of criteria. But initially I started the. Pinegrass 5 prime matrix is. I have taken the five alternatives with the five. Suitable criteria. So this is the first. But habitat to solve by decision making problems. In that case I have entered my input is nothing but linguistic quasi variable. In that perseverable is nothing but. Using the linguistic concept that means. We have the passive, uh, linguistic terms. That means low, medium. I had extremely low on ex medium low. And also the very high, very. Extremely high like that. These are the linguistic. Thank you. Depending upon the users. They can decide the input variable. Restaurant input variable. Uh, we can, uh. Ascend uh assumed our uh. Input uh. Criterias. Agreements after that I have to. Generate the matrices using the matrixes I can. One there mattress is nothing but. Normalized matrices. Using the normalized 2 emphasis, I'm going to use the. Particular, uh. Topsy's technique. I initially started that technique. That means it's not a traditional one. It is Fuzzy Thomas. Instead of uh. We are going to be implement. Fazi naman that means. Uh, why? Because why we need to use the fuzzy number means we have the, uh, situation. In the imprecise situation and also the baggage situation. This this situation we can use the fuzzy fuzzy criteria and also the fuzzy. For the traditional one. We have the fixed value that means pre CS numbers. That means good. So in this world, the full of uncertainty, that's why the many of the places we can't say. Exactly the our traditional. Numbers going to be working there all the situation. But we can't say. Exactly that, traditional numbers. Can I be working the all the situation? So that we are. Going to be used there. Fuzzy. Technique and fuzzy variable. This variable. Uh, it will. Will work in the all the situation. That means when there's a. When the datas may be the implicit or. So value data. In that situation we can use that. Was invariable, so based on the passive variable we can. Uh, finally diversification also we are going. Use using the diversification, it will be finally the crisp number. It will come. We can use the. This is a basic models for.

Okay, so you were working on multi-criteria decision making, building a criteria-versus-alternatives matrix, and applying fuzzy techniques to identify the best option. In practice, when you introduced these fuzzy methods to your research, how did you convince others in your department or field that these were more effective than traditional methods?

Okay, so you built a criteria-versus-alternatives matrix using fuzzy linguistic variables like "very high" or "medium low" to model subjective decision factors — that's a solid application of fuzzy logic in multi-criteria decision making. When you applied these techniques, how did you validate that your chosen alternative was robust and not just a result of the fuzzification choices or weighting scheme you picked?

Okay, you described using fuzzy linguistic terms like "very high" and "medium low" to model imprecise criteria in your decision matrices. Shifting away from research for a moment — when you were teaching or mentoring students, how did you help them grasp these abstract mathematical concepts, especially those who struggled with the formalism?

Okay, so you applied fuzzy multi-criteria decision-making methods to handle situations where criteria and alternatives are subjective or uncertain, like car selection with imprecise factors. Switching gears a bit, when you were teaching, how did you introduce the concept of fuzzy logic and its relevance to students who might only know classical mathematical approaches?

Yes, Sir, it's very amazing questions. Umm, I really impressed. Uh, impressed with. In this question. Uh, yeah, I can explain. It's very simple. Suppose that we have that, uh. Different, uh. Mode of the gears in the core. In the particular scenario. Maybe. Some. The gears are uh. Maybe gear one gate through gate 3? 4th gear first gear, second gear Turgon may be 4th gear. Maybe available in the different types of car? So in that case. The traditional Pazzini mathematical modeling. Will not. OK, absolutely might be work. Because there our Rd. may be different types of. Situation may be ups and downs will be there here. So in that situation. Uh, we can use the. Fuzzy data and also the mathematically. You can use that. Fuzzy approaches. In that case, that means implicit. Suppose maybe on. Uh, certainly some. Animals or dogs? Whatever maybe the birds were going to be. During the uh, during our training. So in that case we can't exactly. That some particular, uh. Again, types we have to slow our cars, then animals can move. Here and there. So in this type of this this type of situation is cutting but value. That means imprecise. We can't predict. It is exactly going to be. We are going to be applying the gear. And also we are going to be. Stop the car in the particular scenario. So that. Uh, we can guess that we can. We can't guess that particular scenario going to be happening. So this is called imprecise situation. In this impressive situation, we can. Make the models. That means we can apply that. Our mind. Using the Wagoner situation. Particular, suppose we have the logic facilitate the FASI. Uh, theory. In the puzzle logic we are using the only zero and one. In the, mathematically maybe 0 is nothing but half or one is nothing but. Going to be on. Many of the places people are using Jurong one that is called puzzle logic. In that facility, not only in the K times, not only in the bar K types, it also works in the all. Our home opinions like. And switch on the fan that means. Check the fan and also the washing machine. French refrigerator in the many places using the fuzzy logic. It is working. Nowadays. But in that. We are updating that. Puzzle logic. That means it's a theory. Between the zero and one, what is the situation? So there is a membership function of the puzzle members. Suppose we have to. In the membership value. Suppose X is the set. That means passes it. We have the element X. X having the fuzzy. Membership function that means new a of gets.

Yes, Sir, it's very amazing questions. Umm, I really impressed. Uh, impressed with. In this question. Uh, yeah, I can explain. It's very simple. Suppose that we have that, uh. Different, uh. Mode of the gears in the core.

Alright, so you were starting to connect fuzzy logic to real-world situations like different modes in a car — I want to switch topics. You mentioned publishing research papers during your PhD. Can you walk me through the review process for one of your publications, and how you responded to critical feedback from reviewers?

Yes, Sir, it's very amazing questions. Umm, I really impressed. Uh, impressed with. In this question. Uh, yeah, I can explain. It's very simple. Suppose that we have that, uh. Different, uh. Mode of the gears in the core. In the particular scenario. Maybe. Some. The gears are uh. Maybe gear one gate through gate 3? 4th gear first gear, second gear Turgon may be 4th gear. Maybe available in the different types of car? So in that case. The traditional Pazzini mathematical modeling. Will not. OK, absolutely might be work. Because there our Rd. may be different types of. Situation may be ups and downs will be there here. So in that situation. Uh, we can use the. Fuzzy data and also the mathematically. You can use that. Fuzzy approaches. In that case, that means implicit. Suppose maybe on. Uh, certainly some. Animals or dogs? Whatever maybe the birds were going to be. During the uh, during our training. So in that case we can't exactly. That some particular, uh. Again, types we have to slow our cars, then animals can move. Here and there. So in this type of this this type of situation is cutting but value. That means imprecise. We can't predict. It is exactly going to be. We are going to be applying the gear. And also we are going to be. Stop the car in the particular scenario. So that. Uh, we can guess that we can. We can't guess that particular scenario going to be happening. So this is called imprecise situation. In this impressive situation, we can. Make the models. That means we can apply that. Our mind. Using the Wagoner situation. Particular, suppose we have the logic facilitate the FASI. Uh, theory. In the puzzle logic we are using the only zero and one. In the, mathematically maybe 0 is nothing but half or one is nothing but. Going to be on. Many of the places people are using Jurong one that is called puzzle logic. In that facility, not only in the K times, not only in the bar K types, it also works in the all. Our home opinions like. And switch on the fan that means. Check the fan and also the washing machine. French refrigerator in the many places using the fuzzy logic. It is working. Nowadays. But in that. We are updating that. Puzzle logic. That means it's a theory. Between the zero and one, what is the situation? So there is a membership function of the puzzle members. Suppose we have to. In the membership value. Suppose X is the set. That means passes it. We have the element X. X having the fuzzy. Membership function that means new a of gets. So each and every element of the X. Having that. Different types of membership function between the zero and one. So this is the basic concept for the mathematical approach to start the to motivate the student. To go with that OK how and every situation in the working. The particular scenario. So we are working basically everyone knows. You don't want. That means zero and one. It will work within all the situation, but what what happened? Between the zero and what? So in this concept we are. Going to be applying the fuzzy. Set theory that means. We had purposive study. He started the. 1969. Started the. And between the zero and one. What are the situation going to be applying? So we are continuing the. That is model that means positive.

Alright, so you’d explain fuzzy logic to students by connecting it to everyday imprecise decisions — like shifting gears in a car when conditions aren’t clear, and relating that to how fuzzy logic works between zero and one, not just on or off. When you taught these concepts, how did you check if your students really understood the difference between classical and fuzzy approaches?

Alright, you emphasized how fuzzy set theory fills the gap between zero and one, helping students grasp uncertainty in real-world systems — makes sense. Shifting to your research publications, could you describe one of your most significant papers, its core contribution, and why you consider it important?

Yes, it's nice questions. Exactly the what is the IT means the research. I can. First of all, I have to introduce the research cap. In that research. There is gap between PASI logic and fascia theory that means. ***** logic between zero and one. Whatever will be the happening in that? Between zero and one. So we are going to be uh. Modulate the modeling the model model fuzzy model that means. In the real world scenario, many of the places situation occurs in. Impressive data. That means the the. The world is full of uncertainty. Maybe, uh, rain will come. May not be rain will come. May not be right. Today suppose. And also there's some other natural. Dishes maybe happen, we can't. Basically predict. Elvis Min Radha's many types of situation, many types of technologies implement even though we, uh, we can't predict. Some of the natural disaster and also the natural. This is that means maybe the corona situation also we can't predict uh. Number of patients, number of people going to be affected there? Particular coronavirus. So it is completely imprecise. In this situation, the rail light complication there. Situation students have to. Understand the concept of how it is going to be spread. Why it is happening? Why? What are the lagging? What are the lagging? What are the situation? Uh, stopping the criteria to uh. To stop the uh. Virus. Disease. On spreading. So these are. Very basic model How to OK stop the coronavirus spreading? So in that situation I have already. The talk with my professor and also the we have published one paper also. How to stop the coronavirus? Spreading these are the. First, initial motivation. To the students.

Yes, it's nice questions. Exactly the what is the IT means the research. I can. First of all, I have to introduce the research cap. In that research.

Yes, it's nice questions. Exactly the what is the IT means the research. I can. First of all, I have to introduce the research cap. In that research. There is gap between PASI logic and fascia theory that means. ***** logic between zero and one. Whatever will be the happening in that? Between zero and one. So we are going to be uh. Modulate the modeling the model model fuzzy model that means. In the real world scenario, many of the places situation occurs in. Impressive data. That means the the. The world is full of uncertainty. Maybe, uh, rain will come. May not be rain will come. May not be right. Today suppose. And also there's some other natural. Dishes maybe happen, we can't. Basically predict. Elvis Min Radha's many types of situation, many types of technologies implement even though we, uh, we can't predict. Some of the natural disaster and also the natural. This is that means maybe the corona situation also we can't predict uh. Number of patients, number of people going to be affected there? Particular coronavirus. So it is completely imprecise. In this situation, the rail light complication there. Situation students have to. Understand the concept of how it is going to be spread. Why it is happening? Why? What are the lagging? What are the lagging? What are the situation? Uh, stopping the criteria to uh. To stop the uh. Virus. Disease. On spreading.

Yes, it's nice questions.

Alright, you’re ready to talk about your most significant paper. Before we pivot, tell me specifically—what was the main result or new contribution of that paper, and how did it move the field forward?

Alright, you were starting to explain the research gap addressed in your most significant paper. Can you walk me through exactly what new result or method you introduced, and why that was valuable to the field?

Yes, it's nice questions. Exactly the what is the IT means the research. I can. First of all, I have to introduce the research cap. In that research. There is gap between PASI logic and fascia theory that means. ***** logic between zero and one. Whatever will be the happening in that? Between zero and one. So we are going to be uh. Modulate the modeling the model model fuzzy model that means. In the real world scenario, many of the places situation occurs in. Impressive data. That means the the. The world is full of uncertainty. Maybe, uh, rain will come. May not be rain will come. May not be right. Today suppose. And also there's some other natural. Dishes maybe happen, we can't. Basically predict. Elvis Min Radha's many types of situation, many types of technologies implement even though we, uh, we can't predict. Some of the natural disaster and also the natural. This is that means maybe the corona situation also we can't predict uh. Number of patients, number of people going to be affected there? Particular coronavirus. So it is completely imprecise. In this situation, the rail light complication there. Situation students have to. Understand the concept of how it is going to be spread. Why it is happening? Why? What are the lagging? What are the lagging? What are the situation? Uh, stopping the criteria to uh. To stop the uh. Virus. Disease. On spreading. So these are. Very basic model How to OK stop the coronavirus spreading? So in that situation I have already. The talk with my professor and also the we have published one paper also. How to stop the coronavirus? Spreading these are the. First, initial motivation. To the students. When you're going to be in the railway situation? It is completely imprecise. In that particular situation, we have to use the fuzzy model. And also puzzle technique. Mathematical modeling the began. Puzzle techniques. In that ***** technique, we can modify the lot of. Parameters and also the depending upon situation. Maybe the? Uh, suppose. We have one particular criteria, some scenario. That scenario. How it is going to be affected? Suppose coronavirus. There is a spreading criteria. Maybe 1 to 1 spreading. Spreading. These are the uh. How to coronavirus spreading mode? So we have implemented lot of all the in the world fully. Implemented the lockdowns. Why we have we need, we need no need to meet the people directly. So maybe if it is directly meeting. Virus going to be spread. So if it is a. The Quarantine 1. We can't. We can reduce the spreading. So that's why they started to quarantine those people, OK. Those who are affected in the. Particular virus. So in that virus, how it is spreading, we have to, uh. Think it is. It is completely imprecise. We can't see the virus going to be spreading. Now so. Some people have very good health even though they have affected by the coronavirus and also they passed away. So this is the mathematical structure. But yeah, students have to, uh, understand the situation. It is fully emphasized. But but that type of situation we have to implement the. Fuzzy technique.

Alright, so you framed your work around handling uncertainty in real-world phenomena, like unpredictable disasters or epidemics, by building fuzzy models. Let's shift focus for a moment — can you share an example of how you've guided a student project or research initiative, and what your role was in supporting their process?

Okay, so you worked with your professor on modeling the spread of coronavirus using fuzzy techniques, and even published a paper on stopping the spread — that’s timely. For that research, what specific mathematical model or framework did you use to capture the uncertainty in infection rates, and how did you validate that your approach reflected reality?

Alright, so your key contribution was applying fuzzy modeling to unpredictable scenarios, like the coronavirus pandemic, where traditional models fall short due to uncertainty and imprecise data. What was the main mathematical innovation or method your paper proposed for handling that uncertainty, and how did you validate its effectiveness?

Yes Sir, I have the. I have handled the. Modeling. How to formulate the mathematics? Mathematical, uh. Equations and also the. How to stop the? Coronavirus. Reading and also the. Quarantine situations. What are the implementation going to be applied in that? Particular scenario. Using the mathematical modeling. We have the First of all, we have to fix the GRA. Parameters. That's a Pixar first of all in Apple. Uh, check the data that that is. Prices are imprecise. If it is, data is precise. We can go for the traditional one. If it is data is imprecise, we can go for the quasi Model 1. Sow to analyse the datas first of all. Suppose uh. We have some Medical Center at. Clinical type. We have to take the uh. Some data. About the patients? What are the PAT? What type of patients? That means? Age group? So how to? Take the edgebook. Suppose maybe the child one, adult one. And olden age people. You have to segregate the data. In the particular data. Suppose old age people have the less immunity and the child also have. The children's also over there lazy money.

Yes Sir, I have the. I have handled the. Modeling. How to formulate the mathematics? Mathematical, uh. Equations and also the. How to stop the? Coronavirus. Reading and also the. Quarantine situations. What are the implementation going to be applied in that? Particular scenario. Using the mathematical modeling. We have the First of all, we have to fix the GRA. Parameters. That's a Pixar first of all in Apple. Uh, check the data that that is. Prices are imprecise. If it is, data is precise. We can go for the traditional one. If it is data is imprecise, we can go for the quasi Model 1. Sow to analyse the datas first of all. Suppose uh. We have some Medical Center at. Clinical type. We have to take the uh. Some data. About the patients?

Yes Sir, I have the. I have handled the. Modeling.

Yes Sir, I have the. I have handled the. Modeling. How to formulate the mathematics? Mathematical, uh. Equations and also the. How to stop the? Coronavirus. Reading and also the. Quarantine situations. What are the implementation going to be applied in that? Particular scenario. Using the mathematical modeling. We have the First of all, we have to fix the GRA. Parameters. That's a Pixar first of all in Apple. Uh, check the data that that is. Prices are imprecise. If it is, data is precise. We can go for the traditional one. If it is data is imprecise, we can go for the quasi Model 1. Sow to analyse the datas first of all. Suppose uh. We have some Medical Center at. Clinical type. We have to take the uh. Some data. About the patients? What are the PAT? What type of patients? That means? Age group? So how to? Take the edgebook. Suppose maybe the child one, adult one. And olden age people. You have to segregate the data. In the particular data. Suppose old age people have the less immunity and the child also have. The children's also over there lazy money. Stamina to uh. To welcome that coronavirus or some other pieces also. So we have disagreed. Suppose maybe the adult may may may have the very good strength to. Welcome that, uh, some disease also. Sahinder Data. You know, to segregate the data and also the particular. And from the data we have to modify the modify the datas. In our fuzzy techniques. That means, suppose we started the mathematical framework. In the. Linguistic. Very. The linguist variable is nothing but. Suppose the coronavirus. Have the list, uh, Simpsons. Symptoms You may have the throat pain. Thing we have to take the initial stage of that. I'm not corona virus spreading like that. So first we have to fix a. Criteria support the adult age. Maybe have the list. Advantage have the. Strong. Efficient to overcome that.

You said you handled the modeling—can you elaborate on the specific mathematical framework or tool you used to model the uncertainty, and how you demonstrated that it worked on real data?

Yes Sir, I have the. I have handled the. Modeling. How to formulate the mathematics? Mathematical, uh. Equations and also the. How to stop the? Coronavirus. Reading and also the. Quarantine situations. What are the implementation going to be applied in that? Particular scenario. Using the mathematical modeling. We have the First of all, we have to fix the GRA. Parameters. That's a Pixar first of all in Apple. Uh, check the data that that is. Prices are imprecise. If it is, data is precise. We can go for the traditional one. If it is data is imprecise, we can go for the quasi Model 1. Sow to analyse the datas first of all. Suppose uh. We have some Medical Center at. Clinical type. We have to take the uh. Some data. About the patients? What are the PAT? What type of patients? That means? Age group? So how to? Take the edgebook. Suppose maybe the child one, adult one. And olden age people. You have to segregate the data. In the particular data. Suppose old age people have the less immunity and the child also have. The children's also over there lazy money. Stamina to uh. To welcome that coronavirus or some other pieces also. So we have disagreed. Suppose maybe the adult may may may have the very good strength to. Welcome that, uh, some disease also. Sahinder Data. You know, to segregate the data and also the particular. And from the data we have to modify the modify the datas. In our fuzzy techniques. That means, suppose we started the mathematical framework. In the. Linguistic. Very. The linguist variable is nothing but. Suppose the coronavirus. Have the list, uh, Simpsons. Symptoms You may have the throat pain. Thing we have to take the initial stage of that. I'm not corona virus spreading like that. So first we have to fix a. Criteria support the adult age. Maybe have the list. Advantage have the. Strong. Efficient to overcome that. Coronavirus. We may have the. Fixed medium. Linguistic variables made you. If. Children's affected by the coronavirus we have to fix. Linguistic terms low. And also the. Old age people may may have the different. They don't have that much stamina to overcome the. Cortana, where is we have to. Fix that. Linguistic terms is nothing but extremely low. So extremely low. Low and also the medium. These are the. First initial stage 2. Overcome. To formulate the matrices. In that mattresses. They have to interlace the. Uh, fuzzy technique. The PASI technique is nothing but based on the. Fuzzy variable. The passive variable gives in the terms of fuzzy numbers. Maybe the traffic struggle was number, maybe the triangular ***** number. And also the payout.

Alright, you started to explain how you’d distinguish between precise and imprecise data and choose your modeling approach accordingly, using fuzzy models for uncertainty. How did you actually validate your model — did you test it on real patient data or use simulations, and what did you find?

So you started by analyzing whether your data is precise or imprecise, then used fuzzy models for uncertainty—especially in complex cases like separating age groups with different immunity profiles during COVID. Did you test your model with real clinical or epidemiological data, and if so, what was one specific insight or result you found when applying it?

Yes Sir, I have the. I have handled the. Modeling. How to formulate the mathematics? Mathematical, uh. Equations and also the. How to stop the? Coronavirus. Reading and also the. Quarantine situations. What are the implementation going to be applied in that? Particular scenario. Using the mathematical modeling. We have the First of all, we have to fix the GRA. Parameters. That's a Pixar first of all in Apple. Uh, check the data that that is. Prices are imprecise. If it is, data is precise. We can go for the traditional one. If it is data is imprecise, we can go for the quasi Model 1. Sow to analyse the datas first of all. Suppose uh. We have some Medical Center at. Clinical type. We have to take the uh. Some data. About the patients? What are the PAT? What type of patients? That means? Age group? So how to? Take the edgebook. Suppose maybe the child one, adult one. And olden age people. You have to segregate the data. In the particular data. Suppose old age people have the less immunity and the child also have. The children's also over there lazy money. Stamina to uh. To welcome that coronavirus or some other pieces also. So we have disagreed. Suppose maybe the adult may may may have the very good strength to. Welcome that, uh, some disease also. Sahinder Data. You know, to segregate the data and also the particular. And from the data we have to modify the modify the datas. In our fuzzy techniques. That means, suppose we started the mathematical framework. In the. Linguistic. Very. The linguist variable is nothing but. Suppose the coronavirus. Have the list, uh, Simpsons. Symptoms You may have the throat pain. Thing we have to take the initial stage of that. I'm not corona virus spreading like that. So first we have to fix a. Criteria support the adult age. Maybe have the list. Advantage have the. Strong. Efficient to overcome that. Coronavirus. We may have the. Fixed medium. Linguistic variables made you. If. Children's affected by the coronavirus we have to fix. Linguistic terms low. And also the. Old age people may may have the different. They don't have that much stamina to overcome the. Cortana, where is we have to. Fix that. Linguistic terms is nothing but extremely low. So extremely low. Low and also the medium. These are the. First initial stage 2. Overcome. To formulate the matrices. In that mattresses. They have to interlace the. Uh, fuzzy technique. The PASI technique is nothing but based on the. Fuzzy variable. The passive variable gives in the terms of fuzzy numbers. Maybe the traffic struggle was number, maybe the triangular ***** number. And also the payout. In that puzzle number. We can make the ranking system. What is the ranking system means? The fuzzy numbers having the lower bound and upper bound. The lower bound is nothing but lower bound is continuous and. Particular thing. This uh, suppose triangular version number. It will. Make the sense the triangular shape. The first ship left hand side to left side 2. That means left spread and right spread. And also the middle is nothing but. The average 1. So left thread and right script going to be eventually. So it will be. ABC. So the part. The particular scenario. We have to fixing that. Pakistan to machinima when the triangular basin when the impressive is less. You can use the triangular question number if it is the. Impressive situation is more. The data is that the data has a lot of impressive situation. We can use that. Bigger puzzle number that means. Trying and also the interval. So in that mathematical framework we have to find the ranking system that means the ranking of A. Calendar site. And also the way and the position to have the puzzle number speaking. Ranking RFA equal to. Banking of. Boundless lighting up. Upper bound divided by two. Ranking of lower bound. Plus ranking of upper one database 2. And we can modify that. We can ranking the. Alternatives. Linguistic terms. Then we can go for. Uh, fuzzy technique. Uh, the fuzzy. Uh, top six technique. In that pasi toxic technique. We can put put it in. Trust mattress is nothing but. Fuzzy lattices. After that we can go for the normalization matrix, then the normalization matrix. We can find, we need to find that. Puzzle solution and negative ideal solution. Then we can compare the. Creative closeness coefficient. Based on the. Process coefficient. We can rank that our. Under 80s, that is the uh. First prayer maturity framework.

Right, so you categorized your patient data—like age groups and symptoms—and decided which mathematical model to apply based on whether the data was precise or imprecise. Let's shift a bit—can you talk about your experience guiding student research projects and how you help students pick a meaningful topic in applied mathematics or optimization?

So you used linguistic variables like age group and symptom severity, then built fuzzy matrices to represent uncertainty in your epidemiological modeling. Shifting focus, Dr. Dharmalingam, tell me about your approach to evaluating students' understanding in a theory-heavy mathematics course — what's your method for ensuring assessments go beyond rote memorization?

So you used triangular and other fuzzy numbers to handle varying degrees of uncertainty in your pandemic modeling, setting linguistic variables for age and symptoms, then ranking alternatives using fuzzy TOPSIS — that's quite detailed. Now, stepping back from research, you've taught both theory and lab courses — how did you design laboratory sessions for students so they'd truly grasp these mathematical concepts in practice?

So. Now they're stepping back from research and also the how to. To teach the lab course. And a student can practice that. The mathematical modeling. You're asking. So now I understand that question. Suppose I am handling the exactly. Puzzle Laboratory also.

So. Now they're stepping back from research and also the how to. To teach the lab course. And a student can practice that. The mathematical modeling. You're asking. So now I understand that question. Suppose I am handling the exactly. Puzzle Laboratory also. We have a in my research work. Have that I see. Concept and also the fuzzy. Laboratory course. That means. Uh, fasi technique with TE. Mathematical model. In that laboratory courses. We have the. Uh, syllabus and also the frameworks. How to do the How to enter the ***** logic in? Maybe the control system, Maybe the control system and all the. ***** controlling. And also they're fuzzy. Basic applications. How to implement the ***** technique in there? Basic models. And also there how to. Uh, upgrade our system into the? Royal Level 2, Iron Level. That means low low volume to higher volume. So suppose. The students may. Very, very new to the party. Techniques are also new to the very. Lapse 2. Start the Premier. That means they need to gather the knowledge about their fuzzy technique. And also the what is the use of puzzle technique And also also what is the? What is mean by fussy? And also what is the? Role of Fasiq in the. I would like to join.

So. Now they're stepping back from research and also the how to. To teach the lab course. And a student can practice that. The mathematical modeling. You're asking. So now I understand that question. Suppose I am handling the exactly. Puzzle Laboratory also. We have a in my research work. Have that I see. Concept and also the fuzzy. Laboratory course. That means. Uh, fasi technique with TE. Mathematical model.

Okay, so you're saying you've actually run fuzzy logic lab sessions. Walk me through how you set up a specific lab exercise — what was the problem you gave the students, and what steps did you have them follow to connect the math to a practical outcome?

Okay, so you’ve actually taught a fuzzy logic lab — for one of those sessions, what was the exact hands-on problem you gave students, and how did you structure the steps so they’d build intuition rather than just follow a recipe?

So. Now they're stepping back from research and also the how to. To teach the lab course. And a student can practice that. The mathematical modeling. You're asking. So now I understand that question. Suppose I am handling the exactly. Puzzle Laboratory also. We have a in my research work. Have that I see. Concept and also the fuzzy. Laboratory course. That means. Uh, fasi technique with TE. Mathematical model. In that laboratory courses. We have the. Uh, syllabus and also the frameworks. How to do the How to enter the ***** logic in? Maybe the control system, Maybe the control system and all the. ***** controlling. And also they're fuzzy. Basic applications. How to implement the ***** technique in there? Basic models. And also there how to. Uh, upgrade our system into the? Royal Level 2, Iron Level. That means low low volume to higher volume. So suppose. The students may. Very, very new to the party. Techniques are also new to the very. Lapse 2. Start the Premier. That means they need to gather the knowledge about their fuzzy technique. And also the what is the use of puzzle technique And also also what is the? What is mean by fussy? And also what is the? Role of Fasiq in the. I would like to join. After that they can enter in the. They may have the sun. Operations technique that means addition of. Remember that. Some of these things tenure. There may be very no. After the Deccan, go for the. Uh, simulation. That means. Suppose we have there's some inbuilt function in the laboratory like. So in the ***** model also having some input functions that means. Fuzzy variable. To analyze the variable. Formulate the variable. And also the. Syntax. These are the things they can. Need to practice. Then we can. Go for the small small problem. Uh, based on a small problem, they can. Trying to. OK, practice. Some high level problems also. So this is the very basic. Technique and now currently I'm working the R program. So I have working the. Theatrical methods with R programming. There are also the students can. Easily understand the concept. What is the basic value? It's how to handle the variables. What type of? Variable how to generate the? How to create the? Put out to check it there. So. What are the outputs? It happened before. Uh, entering. The problem we have to think it. What is our requirement? What is our question? What is your input? What are what type of output going to be get? So this is the very basic concept. Suppose we are going to find the. Average of phone number. We know the average of tournaments. What about the average of N number of? We can use. And syntax in that. Particular move. Method mathematical modeling. Start skill modeling. Whatever so that also have the. Inbuilt functions. Some inbuilt function. We can modify the uh. Terms. What are the supposed ranking of correlation coefficient? How to find the correlation coefficient? Understands climbers. These things students can learn from the theoretical subject. Then we can go for that. Laboratory where students can easily uh. Interpretation. You should get the interpretation and also they can implement the. Laboratory Courses. Now they're all doing very good. E-mail apparently on. So we have the lab. What's the student interact with me? How to solve the problems? The sense key methods concept. So now I'm next semester I'm going to implement puzzle technique concept. How do you? That's good, That's all.

Alright, so you mentioned starting with the basics of fuzzy logic and gradually introducing control system applications in the lab. Let's switch topics: when it comes to evaluating students—especially in mathematical modeling or lab work—how do you ensure your assessments are fair and actually measure deep understanding rather than just rote memorization?

Alright, you described how you start with foundational fuzzy logic concepts, use R programming for hands-on modeling, and guide students through both theoretical and practical lab exercises — covering everything from syntax to real applications. Shifting topics, Professor, can you walk me through how you evaluate student performance, especially in mathematics lab courses where solutions can be open-ended or computational?

Nice, uh, very nice question. Umm, we can evaluate the students, uh. Based on their assessment. That means lab assessment. How they're, uh, giving the uh. Reference to the elaborate course. How much they're interested in the laboratory process and also how much. Interested in the laboratory? We have to predict their knowledge. Either the students are really involving. Their particular laboratory hours and also their. Uh, really? They are doing the programs. They're trying to get the output or not. You have to check it. And we have to give the small small uh. Tips and incentives to the student. Get the ECE output. Baby students can get in trouble and discarded newly. We can do the small, small tips, we can guide them. What is the simple way out? Or what is the simple way to get the? Good. And also that. How to elaborate the? Their knowledge and also we can. Continuous monitor can we can easily access our students and also we can put the. Uh, ranking the. How much they are? Clean the laboratory concerns. And how much they understand. What are the? They're affecting uh. They're they're not able to. That's a particular problem, even the simple program. Some students, they are not able to. To solve the problem. But nowadays they help. Lot of resources and also they have a lot of. My devices they have. We can easily assess the questions. How to get? But how to clear the doubts? Even though they may hesitate to talk with them. Professor, they may. Thinking, uh. They can using the mobile phone where? Trying to look again. Checking that questions and also getting there some particular favorable answers. Many of the students. They're outsourcing that means. Apart from our classroom teaching, they are getting more knowledge from. That, uh, other devices that means mobile phone and. The laptop using. Uh, Internet. They are getting a lot of information. And what is the scenario means? Either they are working or not. In the laboratory we have to check in continuous modicum, continuous monitor. We can predict our student is good. Are low or medium. Based on that their performance developer covers they're getting out. We have to check in continuously. We can get the signature in that particular output. They are interested in serving the result or not. We have to check it in. Continuous monitor can help the students. Uh, they're, they're coming out. We can access our. Can. Students performing.

Nice, uh, very nice question. Umm, we can evaluate the students, uh. Based on their assessment. That means lab assessment. How they're, uh, giving the uh. Reference to the elaborate course. How much they're interested in the laboratory process and also how much. Interested in the laboratory? We have to predict their knowledge. Either the students are really involving. Their particular laboratory hours and also their. Uh, really? They are doing the programs. They're trying to get the output or not. You have to check it. And we have to give the small small uh. Tips and incentives to the student. Get the ECE output. Baby students can get in trouble and discarded newly. We can do the small, small tips, we can guide them. What is the simple way out? Or what is the simple way to get the? Good. And also that. How to elaborate the? Their knowledge and also we can. Continuous monitor can we can easily access our students and also we can put the. Uh, ranking the. How much they are? Clean the laboratory concerns. And how much they understand. What are the? They're affecting uh. They're they're not able to. That's a particular problem, even the simple program. Some students, they are not able to. To solve the problem.

Nice, uh, very nice question. Umm, we can evaluate the students, uh. Based on their assessment. That means lab assessment. How they're, uh, giving the uh. Reference to the elaborate course. How much they're interested in the laboratory process and also how much. Interested in the laboratory?

So you look at not just their lab work but also their engagement and interest — that's useful. When a student struggles with an open-ended computational problem and submits an incomplete solution, how do you give feedback to help them improve without simply giving them the answer?

Alright, so you're looking at both their engagement and their actual lab work to judge understanding — and you mentioned offering small tips when they're stuck. Say a student keeps missing key steps in their solution but doesn't ask for help, how do you intervene without just giving them the answer?

Yes, Sir. Thank you. So like, uh. Google.

Okay, Dr. Dharmalingam, sounds like you've got a real hands-on approach and care about student engagement as well as their technical work. That's all for today — appreciate your detailed responses and your time. Take care.

Yes, Sir. Thank you. So like, uh. Google. Shall I close my Google meeting? Hello, Sir. Hello.

Alright, Dr. Dharmalingam, yes, you can close your Google meeting now. Thank you for taking the time to speak with me and for sharing your experiences and insights.