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1st ACm Virtual Global Computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Indra, R. Parthasarathy, P.D. Ambasana, Jatin Satavlekar, Spruha Department of Information Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Computer Science and Information Systems Bits Pilani KK Birla Goa Campus Goa India Centre for Educational Technology Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9798400705984

machine learning (mL) course for undergraduates face challenges in assessing student learning and providing practical exposure. Group project-based learning, an increasingly popular form of experiential learning in CS education, encounters certain limitation in participation and non-participation from a few students. Studies also suggest that students find longer programming assignments and project-based assessments distracting and struggle to maintain focus when they coincide with other courses. To tackle these issues, we introduced FlashHack: a monitored, incremental, in-classroom micro Hackathon that combines project-based learning with Hackathon elements. Engaging 229 third year CS undergraduate students in teams of four, FlashHack prompted them to tackle predefined challenges using machine learning techniques within a set timeframe. Assessment criteria emphasized machine learning application, problem-solving, collaboration, and creativity. Our results indicate high student engagement and satisfaction, alongside simplified assessment processes for instructors. This experience report outlines the Hackathon design and implementation, highlights successes and areas for improvement making it feasible for replication by interested computing educators. © 2024 ACm.

关键词： Adversarial machine learning

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Attention-based battery Parameter Augmentation Technique to Alleviate Limited Data Challenges

Attention-based Battery Parameter Augmentation Technique to ...

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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： maiya, Vageesh Channegowda, Janamejaya Lingaraj, Chaitanya B.M.S College of Engineering Karnataka Bengaluru India M S Ramaiah Institute of Technology Karnataka Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Lithium-ion batteries have proven to be a reliable and energy dense source for most of the commercial consumer electronics devices. Increase in the number of battery-powered Electric Vehicles (EVs) have further fueled the need to produce energy-dense batteries to improve the range of these vehicles. State-of-Charge computation techniques have amassed considerable interest among battery researchers. Precise State-of-Charge values help to asses the remaining range of an EV. Unfortunately, access to proprietary battery datasets have restricted research in this area. Furthermore, open access datasets lack the heterogeneity needed to emulate real-world battery discharge profiles. This paper aims to resolve challenges posed by limited data by employing an attention-based mechanism to produce battery synthetic datasets. The introduced scheme was used to produce synthetic Capacity, Voltage & Current datasets. We employ mean Absolute Error (mAE) and mean Squared Error (mSE) metrics to evaluate the efficacy of this data augmentation approach. The implementation of this technique is available at this URL: https://***/vageeshmaiya/Attention-based-Synthetic-battery-Data-Augmentation-Technique © 2022 IEEE.

关键词： mean square error

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Seismic response prediction of asymmetric structures with SmA dampers using machine learning algorithms

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Asian Journal of Civil engineering 2025年第6期26卷 2475-2497页

作者： Parghi, Anant Gohel, Jay Rastogi, Apurwa Yucel, melda Avci-Karatas, Cigdem mevada, Snehal Department of Civil Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395007 India Department of Civil Engineering İstanbul Aydin University Küçükçekmece Istanbul 34295 Turkey Department of Transportation Engineering Faculty of Engineering Yalova University Yalova 77200 Turkey Structural Engineering Department B.V.M. Engineering College Gujarat Anand (Vallabh-Vidyanagar) India

The dynamic response of asymmetric structures to seismic forces is challenging due to mass, stiffness, and damping distribution irregularities. Shape memory alloy (SmA) dampers have successfully dealt with these issues because of their distinctive super elasticity and energy dissipation characteristics. In this work, we study regression algorithms’ effectiveness in predicting the seismic behavior of asymmetric structures installed with SmA dampers. A numerical simulation produces a comprehensive dataset of structural parameters consisting of the structure’s varying periods, frequency ratios, and eccentricity ratios. The critical responses of structures, including lateral and torsional displacement, lateral and torsional acceleration, and stiff and flexible edge damper forces, are predicted using machine learning (mL) techniques, artificial neural networks, decision trees, support vector machines, ensemble bagged trees, and Gaussian process regression. The model is validated using performance metrics such as mean absolute error and root mean square error, mean absolute percentage error, coefficient of determination, and Shapley Additive explanations values, ensuring that predictions are robust and consistent. The results revealed that regression methods accurately model the nonlinear dynamic behavior of SmA dampers in asymmetric structures, providing exact and computationally efficient predictions of seismic response. This predictive paradigm facilitates optimal damper configuration, minimizing the computational complexity of iterative design methods. The proposed research integrates advanced materials with mL methods to create seismically resilient structural systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Advanced materials Asymmetric structure machine learning Regression algorithms Seismic response prediction Shape memory alloys Structural eccentricity

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Experimental Condensation Characteristics, Using HFC Refrigerants in micro-fin Tubes 1

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2nd biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Singh, Sanjeev Kukreja, Rajeev Singh, Ajay Partap Sharma, Ravish Singh, Shravan Kumar Nimesh, Priyanka Singh, Sukhdeep Department of Mechanical Engineering I.I.M.T. University U.P Meerut India Department of Mechanical Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India

ISBN: (数字)9789819724819

ISBN: (纸本)9789819724802

micro-fin (mF) tubes, which are utilized in condensers and evaporators in heating and cooling systems, are employed in these components. In the current work, experimental investigations are made of the frictional pressure drop (PD) and heat transfer coefficients (HTC) while the condensation of hydro-fluoro-carbon (HFC) refrigerants. Horizontal smooth tube (ST) and micro-fin (mF, 15˚ and 18˚ helix angles, 9.52 mm OD) tubes vapour quality of 0.1 to 0.9 are used in experiments, respectively. The computed local condensation HTCs for HFC refrigerants inside of the mF tube are 1.21–1.82 and 1.18–1.54 times as large as the smooth tube (ST). Refrigerant R-134a has higher HTCs and frictional PDs than R-404A both in smooth tubes as well as micro-fin tubes. A new correlation has been proposed for calculating HTCs in micro-fin tubes by using experimental data of this study. Comparison of experimental data has been made with predicted values obtained from correlations proposed by other similar research studies. For refrigerants R-134a and R-404A, oil concentrations ranged between 1.31 and 1.55%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Drops

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Developing and Implementing An Effective big Data Classification System 2

Developing and Implementing An Effective Big Data Classifica...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： managuli, manjunath Pujar, Sagar managuli, Sangamesh C Krishnamurthy, K.T. Nandalike, Rajesh Kunchur, Pavan Kls Gogte Institute of Technology Dept. of Electronics & Communication Belagavi India Kls Gogte Institute of Technology Dept. of Computer Science Belagavi India Pes University Dept. of Mechanical Engineering Bangalore India B M S College of Engineering Dept. of Electronics & Instrumentation Bangalore India Nitte Meenakshi Institute of Technology Dept. of Electronics & Communication Bangalore India

ISBN: (纸本)9798350317558

Relevant data technology advancements have led to a massive growth in classifications. Deep learning systems, however, need help handling such a massive volume of data. This work aims to use the Evaluation of different classification algorithms for big data, Comparison of accuracy, precision, recall, and F1 score among various algorithms, and Identification of algorithms suitable for handling large-scale datasets and conventional deep learning to analyze a large amount of data. Classifiers allowed researchers to examine how various random sampling strategies affected the model's performance. Two methods of feature selection were employed in the tests. The bayesian Network likewise employs a single deep learning classifier. by lowering the dimensionality of the data, this study seeks to maximize model performance. A recording dataset larger than 60 Gb was made less dimensional to conduct the trials. The findings demonstrate that, when employing both sampled and non-sample data, overall performance varies between 78% accuracy and 82% accuracy, a minor variation in performance while utilizing both sampled and non-sample (all the data) datasets. The random subset approach and the bayesian network classifier were employed in the trials, and the overall findings show that the variety experiment had the best performance out of all the combinations of experiments. Test Cradle Experimentation: Variation length, delay variation, and packet loss are used to assess the predicted scheme's efficiency. © 2024 IEEE.

关键词： bayesian Network deep learning feature classification Feature selection

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An AI based Chat bot For banking Applications using Intelligent Chat bots

An AI Based Chat Bot For Banking Applications using Intellig...

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2023 IEEE International Conference on Research methodologies in Knowledge management, Artificial Intelligence and Telecommunication engineering, RmKmATE 2023

作者： Kira, Ajmeera Vidhya, Veda Kamesh, D.b.K. Srilakshmi, V. Paulraj, D. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of CSE Malla Reddy Engineering College for Women maisammaguda hyderabad 500100 India B V Raju Institute of Technology India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India

ISBN: (纸本)9798350305708

A chat and voice assistant that is powered by AI and is integrated into a banking application. This assistant translates data in such a way that the text and audio are designed to simulate human conversations. Since their inception, voice and chat assistants have seen substantial technological advancement. The advancements that have been made in machine learning and natural language processing have made it possible for them to now learn from their experiences and conduct conversations that are more human-like. because of their more human nature, they are able to assist with a wide variety of tasks, such as calculating loan interests, confirming facts regarding transactions, and checking savings. © 2023 IEEE.

关键词：

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Prediction of Heart Disease Using Hybrid Naïve bayes Technique 22

Prediction of Heart Disease Using Hybrid Naïve Bayes Techni...

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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Patil, Shantakumar Lutimath, Nagaraj m. Jogish, D. Premjyoti Patil, bhargav S. Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bengaluru India Bengaluru India B.M.S. College of Engineering Computer Science and Engineering Karnataka Bengaluru560019 India

ISBN: (纸本)9781665457316

The domain of medical diagnosis has attracted many researchers. Several cases of human early mortality have been predicted by investigating the diseases. Different reasons are the cause of several diseases and one such cause is heart disease. Set of disorders in the heart lead to heart disease. It consists of blood vessel issues such as irregular heart beat problems, weak heart muscle problems, congenital heart difficulties, cardio vascular defects and coronary artery disorder. many researchers have summarized idiosyncratic procedures to preserve human life and help health care experts to recognize, prevent and manage heart disorder. machine Learning is utilized for decision making in many domains. Naive bayes technique is one approach which uses conditional probability. In this paper we have taken UCI machine learning data repository including of patients affected from heart disease is analyzed using Complement Naive bayes probability methodology along with correlated features of the heart disease data set. The prediction is done using this concept as hybrid Naive bayes probability technique. The advantage of Complement Naive bayes probability prediction is that, it can be used for imbalanced data set. Accuracy of classification of patients suffering from heart disease is predicted. Implementation is done in Python environment. © 2023 IEEE.

关键词： Python

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Compensator Design for magnetic Levitation System 1

Compensator Design for Magnetic Levitation System

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1st International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

作者： Astik, mitesh b. bhatt, Shital D. Joshi, minesh K. modha, Paresh R. A.D. Patel Institute of Technology Department of Electrical Engineering Gujarat Anand India M.B. Patel Institute of Technology Department of Electrical Engineering Gujarat Anand India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

In this paper, the mathematical modeling of magnetic levitation has been discussed. The magneto motive Force (mmF) produced by the electromagnet is used to levitate the ferromagnetic ball in the air. The integrated position sensor circuitry detects the location of the ball, which is then utilized as a feedback signal to regulate the position of the ball. The experiment's goal is to create a controller that allows the ball to levitate at its equilibrium position. Proportional Derivative (PD) controller works as a compensator design for magnetic levitation which makes the overall system stable. The system stability is checked by the root locus. The step, square and sine response for uncompensated and compensated systems are carried out using matrix Laboratory (mATALb) simulation. moreover, the system is controllable and observable as they were verified using the condition of the controllability and observability matrix. © 2022 IEEE.

关键词： magnetic levitation

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Experimental and Analytical Comparative Analysis on Effectiveness of Different Wrapping Techniques for Two Way RC Slabs Using Different International Guidelines 3rd

Experimental and Analytical Comparative Analysis on Effectiv...

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3rd International Conference on Civil engineering Trends and Challenges for Sustainability, CTCS 2021

作者： Shubhalakshmi, b.S. Jagannath Reddy, H.N. Prabhakara, R. Kasi, Arjun Dayananda Sagar Academy of Technology and Management Karnataka Bangalore India Bangalore Institute of Technology Karnataka Bangalore India Structural Engineering VTU Karnataka Belagavi India B.M.S. College of Engineering Karnataka Bangalore India

ISBN: (纸本)9789811918612

Enhancement of strength and performance of building members is the need of hour for the existing structures. Every time the slab strengthening is the most challenging task, since very few techniques are available as on date and the productivity of those are under research stage (Hollaway in Constr build mater 24:2419–2445, 2010) [1]. In this scenario, fibre reinforced polymer composites promotes the research activity due to the various advantages of these materials over steel (Anakal et al., in Int J Adv Sci Eng Technol 6(1), 2018) [2]. The Fibre Reinforced Composites (FRP) such as CFRP (Carbon Fibre Reinforced Polymer) composite and GFRP (Glass Fibre Reinforced Polymer) composite materials used, seems to be not so economical in the present situation of technology and its application. However, basalt fibres which are very much used in different engineering fields and their application are limited in Structural and construction industries. In this work, strengthening of RC slabs was done using wrapping technique using basalt Fibre Reinforced Polymer (bFRP) composites by means of hand layup method and wet layup technique. Unidirectional knitted fibres were attached to the surface of the slab in different configurations (plus and square), in a way that the FRP and the RC slab performs as single structure when subjected to load. The effectiveness of the retrofitting method was measured by means of the deflection and crack width on working loads attained by the RC slabs. The deflection and crack width on working loads obtained from the experiment were compared with guidelines of ACI 440.2R-17 (ACI 440.2R-17, Guide for the design and construction of externally bonded FRP systems for strengthening concrete structures) [3], ISIS CANADA Design manual No. 3 (ISIS CANADA Design manual No. 3 2007, Reinforcing concrete structures with fibre reinforced polymers) [4], Fib bulletin14 (Fib bulletin14-2001, Externally bonded FRP reinforcement for RC structures) [5], CNR DT 200 R1/201

关键词： Concrete construction

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management in Industrial Sectors using Neuro-Fuzzy Controller and Deep Learning 3

Management in Industrial Sectors using Neuro-Fuzzy Controlle...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sabapathi, D. Pawar, Yogesh Shivaji Patnaik, Sumagna Sivanantham, E. Prabhu, D. Karthik Prakash, N.b. M P Nachimuthu M Jaganathan Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Erode India Bhabha University Bhopal Department of Electrical Engineering Madhya Pradesh Bhopal India J.B. Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Ramco Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Rajapalayam India National Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Kovilpatti India

ISBN: (纸本)9781665462167

Load forecasting plays a vital role in generation and distribution sectors in the power system. This helps to obtain optimum load scheduling which helps to predict future consumption to increase reliability in the system. The demand side management helps to optimize the consumption of energy based upon the priority of the consumers. The load forecasting helps to predict the usage of power through the priority scheduling of the loads which helps to minimize and maximize the operating cost. The optimization technique plays a versatile role in the load scheduling based on demand side management in the industrial sectors. The combination of advanced technologies with communication infrastructure makes the system more reliable and smarter. The demand side management is achieved through shifting the loads from peak hours to non-peak hours. Thus, to enhance the automatic scheduling of loads in the industrial sector is achieved by the neuro-fuzzy controller and deep learning techniques. © 2023 IEEE.

关键词： Forecasting

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