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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shetty, Keerthi Mudholkar, Megha Mudholkar, Pankaj Muralidhar, L.B. Swamy, T.N. Lakshmikanth, R. Department of Mca Udupi India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India School of Commerce Department of Bms Karnataka Bengaluru India Dr. Ambedkar Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Adithya Institute of Technology Department of Management Studies Coimbatore India

ISBN: (纸本)9798350364699

Increasing the efficiency of office spaces through the implementation of technology that is connected to the Internet of Things (IoT). Through the use of Internet of Things bias, the research provides a comprehensive framework for optimising space application, perfecting energy effectiveness, and enhancing overall plant production throughout the facility. To cover and control many aspects of the workplace environment, including lighting, heating, cooling, and occupancy, the frame combines intelligent detectors, real-time data analytics, and automated systems. The approach allows for dynamic changes to office conditions that are based on current operation patterns. This brings about a reduction in functional costs and a reduction in energy waste. The approach is based on the analysis of data obtained from Internet of Things bias. In addition, the investigation investigates the perpetration issues and implicit benefits of the results of the Internet of Things, focussing on the impact that these results have on both functional effectiveness and hand pleasure. According to the findings, Internet of Things (IoT) -Enabled operation systems have the potential to drastically transform conventional office environments into workspaces that are more adaptable, efficient, and responsive. This would pave the way for future developments in smart structure technologies. © 2024 IEEE.

关键词： Information management

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Green Machine Learning Approach for QoS Improvement in Cellular Communications 2

Green Machine Learning Approach for QoS Improvement in Cellu...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Saeed, Mamoon M. Saeed, Rashid A. Azim, Mohammad Abdul Ali, Elmustafa Sayed Mokhtar, Rania A. Khalifa, Othman Faculty of Engineering Communications and Electronics Engineering Department Yemen Taif University College of Computers and Information Technology Department of Computer Engineering Taif21944 Saudi Arabia Asian University for Women Department of Computer Science Bangladesh Department of Electrical Electronic Engineering Port Sudan Sudan College of Electronics Engineering Khartoum Sudan International Islamic University Malaysia Faculty of Engineering Dept of Electrical and Electronics Engineering Malaysia

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Green cellular communications are becoming an important approach due to large-scale and complex radio networks. Due to the dynamic cellular network behaviors related to interference distribution, traffic bottlenecks, congestion points, and hotspots, there is a need to evaluate the dynamic processes in cellular systems in addition to ensuring spectrum availability. The delay, loss rate, and SNR are the most issues that may affect cellular communication performance. Artificial intelligent algorithms such as machine learning (ML) enable to detection of the dynamics in cellular networks by analyzing the complex cellular network processes and evaluating the spectrum and links qualities. It enables the extraction of spectrum knowledge from the network autonomously. The extracted information helps to know about every dynamic change in wireless parameters, related to frequency, modulation, route selection, etc. This paper provides details about the use of ML in green cellular networks to efficiently upgrade the communications and enhances different related approaches including quality of services (QoS), signal traffic load, and energy efficiency, which are critical issues of green cellular communication paradigms. The paper also presents the technical concept of green ML approaches to solve significant problems in cellular communications, in addition to future aspects and considerations for energy consumption minimization using the green ML approach in cellular radio communications. © 2022 IEEE.

关键词： Energy efficiency

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engineering Educators' Adoption and implementation of Project-Based Learning: Experiences from a South Indian University 13

Engineering Educators' Adoption and implementation of Projec...

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13th IEEE International Conference on Teaching, Assessment and Learning for engineering, TALE 2024

作者： Dijo, Anjal P Anantha, Krishnan K. P. Subheesh, N.P. John, Aby Anzar, S.M. Sachidananthan, Sajin Federal Institute of Science and Technology Department of Computer Science and Engineering India College of Engineering Department of Computer Science and Engineering Kerala India Srm University - Ap Teaching Learning Centre Andhra Pradesh 522502 India Rudn University Faculty of Philology Moscow Russia Tkm College of Engineering Department of Electronics and Communication Engineering Kollam India Curriculum Research and Development U-DNA Educational Foundation Andhra Pradesh Anantapur India

ISBN: (纸本)9798350376234

This research investigates the attitudes and perceptions of engineering educators in Kerala, India, towards the implementation of Project-Based Learning (PjBL) within the framework of APJ Abdul Kalam Technological University (APJAKTU), Kerala. Despite the growing emphasis on experiential learning, only limited research studies focus on understanding the attitudes and perceptions of engineering faculty members towards PjBL. Against this backdrop, the present study aims to critically examine the readiness of engineering educators in adopting and identifying PjBL strategies for its effective integration into engineering curricula. The paper explores various dimensions of engineering educators' engagement with PjBL, including faculty interest levels, satisfaction of implementation, perceived effectiveness, and career implications. The data were collected from engineering faculty members by distributing a comprehensive questionnaire titled Project-Based Learning in engineering Education in Kerala (PLEEK), uniquely designed by the researchers. The research findings reveal that over 90% of the surveyed faculty members have shown keen interest in implementing PjBL, while the remaining respondents exhibited a neutral stance. This underscores a noteworthy receptivity to PjBL among engineering educators in Kerala. Suggestions from participants indicate that, if implemented correctly, PjBL would be highly effective, although time constraints pose a significant challenge. The results obtained are pivotal as they provide an explicit idea of the current status of the engineering education settings and the discrepancies between traditional theoretical instruction and project-based learning. © 2024 IEEE.

关键词： engineering education

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ENCL: Empirical Evaluation of Consumer Behaviour Analysis Model Using Internet of Things (IoT) Based Enhanced Neural Classification Logic

ENCL: Empirical Evaluation of Consumer Behaviour Analysis Mo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Merlin, S. Sudha Malathy, K. Ravi, S. Arun, M. Raut, S.P. Fawareh, H. Dmi College of Engineering Department of Electrical and Electronics Engineering Palanchur Chennai India S.A. Engineering College Department of Mca Chennai India Department of Vlsi Microelectronics Chennai India Kings College of Engineering Dept of Computer Science and Engineering Punalkulam Thanjavur India Yeshwantrao Chavan College of Engineering Department of Civil Engineering Nagpur India Zarqa University Faculty of Information Technology Software Engineering Dept Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The proliferation of consumer data can be attributed to 'the Internet of Things (IoT),' which makes it easier to access and more detailed than before. In this research our purpose is to evaluate the effectiveness of a consumer behavior analysis model using data from IoT. The quality of the model is evaluated on multiple parameters - accuracy, predictability and business sense for marketing. It uses the analytics of huge amounts for data from IoT devices to predict patterns, customer preferences and trends. The proposed method called as Enhanced Neural Classification Logic (ENCL) is cross-validated with the already existing method, which is termed as Consumer behavior Forecasting Strategy (CBFS), and helps users to share gather or process information in a fast and cost-effective way through the proposed method. The results are useful to grasp the promise of IoT in more effective consumer behavior analysis. The research portrays the strengths, weaknesses and possible areas of improvement in this model which will be beneficial for both academic and industry practitioners. As such, the objective of this study is to show how an IoT-based consumer behavior analysis can increase business growth and customer satisfaction. The experimental evaluations demonstrate significant improvements in predictive performance over traditional methods, providing actionable insights for targeted marketing and strategic decision-making. The results highlight ENCL's potential to transform consumer behavior analysis by delivering more precise and nuanced understandings of market dynamics. © 2024 IEEE.

关键词： CBFS Consumer Behavior Data Analysis ENCL Internet of Things IoT Marketing Strategy Model Evaluation

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Examining the Impact of Data Mining on Decision-Making Processes 3

Examining the Impact of Data Mining on Decision-Making Proce...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Singh, Vaishali Pandey, Pratik Aggarwal, Prateek Reena, R. Bhagwat, Suvarna R. Sunil, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Statistics mining has emerged as a crucial device for agencies in the past two decades that significantly impact selection-making methods. It can be used to gain insights from massive datasets and might assist with an employer's marketing approach, operational performance, customer segmentation, etc. This paper will explore the impact information mining has on decision-making approaches, especially regarding coming across new styles and relationships. It will discuss the blessings of using records mining and how it may help decorate choice-making. It will also look at various methods used to extract statistics from massive datasets and how they facilitate the selection-making method. Finally, it will discuss a number of the constraints of information mining and the ability of answers to overcome them. Standard, this paper aims to pick out the effect of information mining on the choice-making system and how it may be leveraged as a practical device. © 2023 IEEE.

关键词： discuss extract information overcome relationships

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Nanostructured Quadric-Confined Quantum Dot–Quantum Well Kesterite Solar Cell: A Pathway for Cost-Effective Photosensitive Devices

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Physica Status Solidi (A) Applications and Materials Science 2025年第0期

作者： Mohanty, Smruti Ranjan Palanisamy, Chandrasekar Sahoo, Sudarsan Routray, Soumyaranjan Department of Electronics and Instrumentation Engineering NIT Silchar Assam Silchar788010 India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul University Gujarat Vadodara391760 India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India

Nanostructure kesterite-based solar with a combination of multi-quantum dots (QDs) and multi-quantum well (QW) region is proposed to improve photoabsorption rate and conversion efficiency. The scope of kesterite hybrid-confined structure attracts researchers due to their environment-friendly, encouraging optic-electrical characteristics and carrier confinement properties. The optimal design of a quadric-confined QDQW solar cell (QCQDQW) focuses on nanostructures that improve the open-circuit voltage Voc. This article examines the overall performance of QCQDQW structure by implementing both QD (width 10 nm) and QW (10 nm barrier and 5 nm well width) layers (CZGSe/ (Formula presented.)) considering S/Se composition variations (x = 0.2–0.8). Moreover, a thin 5 nm barrier layer is engineered between QD and QW layers, enhancing effective carrier quantization and collection in solar cell. The remarkable efficiency of 17.37% and Voc of 0.945 V is obtained for three QW regions and five QD layers with the highest Jsc value of 21.06 mA. This study encourages a new direction in quantum-confined solar structure. © 2025 Wiley-VCH GmbH.

关键词： Semiconductor quantum dots

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A Robust Development of Calorie Prediction Methodology based on Artificial Intelligence Assisted Machine Learning Model 4

A Robust Development of Calorie Prediction Methodology based...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Naga Venkata Ramakrishna, G. Sreelakshmi, S. Diwakar, Meenakshi Ramkumar, S. Vinithra Banu, T. Thilagam, T. University of Technology and Applied Sciences Al Musannah Faculty of Engineering Oman Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Tamil Nadu Chennai India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India S.A Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Modern people's hectic schedules are a direct result of the ways they live and the responsibilities they have at work. On the other hand, maintaining a healthy lifestyle demands consistent physical exertion. Obesity is a result of people not paying attention to what they eat. As a result of modern living, obesity is on the rise. As a result, in order to maintain a healthy weight, people pick their food and exercise habits accordingly. People should know how many calories they consume and how many calories they burn. Calorie counts are easy to get online or on product labels, so it's not hard to keep track of one's calorie intake. There aren't many tools available to help you keep track of the calories you burn. To keep up with the hectic pace of modern life and the constant search for shortcuts, we built a system that uses machine learning to track post-workout calorie burn and development. By taking a few attributes as input, this system can approximate the calories burned, which will show daily growth and motivate people to exercise more. This paper presents an innovative ML model for accurate calorie prediction;it's called the Improved Learning Model for Calorie Prediction (ILMCP). To test how well it works, the authors cross-validate it with the traditional Extreme Gradient Boosting (XGBoost) algorithm. As a result, it is the quickest approach and may be utilized for object identification in real-time. Next, we'll apply the ILMCP algorithm to the picture for segmentation. Foreground extraction with little to no user input requires it. Following picture segmentation, we use the probing object's known volume to determine the food item's volume. Once the volume has been determined, the food item's mass may be determined using formulae. Then, the connection between weight and calories can be used to compute the food item's calories. © 2024 IEEE.

关键词： Image segmentation

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Possibilities of the Investigations of the Thermal Parameters of the Polybutyl Methacrylate Siloxane Coatings with the Use of the Photothermal Radiometry 25

Possibilities of the Investigations of the Thermal Parameter...

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25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2024

作者： Chrobak, L. Malinski, M. Korte, D. Janta-Lipinska, S. Koszalin University of Technology Faculty of Electronics and Computer Science Sniadeckich 2 Koszalin75-453 Poland University of Nova Gorica Laboratory for Environmental and Life Sciences Vipavska cesta 13 Nova Gorica5000 Slovenia Koszalin University of Technology Faculty of Civil Engineering Environmental and Geodetic Science Sniadeckich 2 Koszalin75-453 Poland

ISBN: (纸本)9798350393637

This article presents the results of theoretical and experimental investigations of thermal parameters of Polybutyl Methacrylate Siloxane (PBMA-siloxane) anticorrosion coatings by the use of the Photothermal Radiometry (PTR) method. The goal of the study was to investigate possibilities of application of this method for the thermal characterization of PBMA-siloxane coatings. Three different theoretical models of the PTR signal describing the two-layer system of the analyzed coatings deposited on a substrate have been discussed and investigated. The character of the experimental results was interpreted in the framework of the model that exhibits their best reproduction enabling the extraction of the thermal parameters of the coatings. © 2024 IEEE.

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Inflated 3D ConvNet for detection of sign language 4

Inflated 3D ConvNet for detection of sign language

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4th International Conference on Signal Processing and Machine Learning, CONF-SPML 2024

作者： Chandana, D. Tushara, M. Sri, A. Ramya Sakhamuri, Sridevi Abualigah, Laith Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram522502 India Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman Jordan

ISBN: (纸本)9781510674721

Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can’t speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words. © 2024 SPIE.

关键词： Deep neural networks

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Structural RC computer aided intelligent analysis and computational performance via experimental investigations

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Structural engineering and Mechanics 2024年第3期90卷 253-261页

作者： Huang, Y.C. Lulios, TuMuli Chang, Chu-Ho Noor, M. Nasir Shao, Jen-Chung Chiu, Chien-Liang Lee, Tsair-Fwu Wang, Renata Information Technology Faculty Duy Tân University 254 Nguyen Van Linh Danang Viet Nam Princess Margaret Hospital Funafuti Island Private Mail Bag Tuvalu College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan College of Maritime National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Medical Physics and Informatics Laboratory of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This research explores a new finite element model for the free vibration analysis of bi-directional functionally graded (BDFG) beams. The model is based on an efficient higher-order shear deformation beam theory that incorporates a trigonometric warping function for both transverse shear deformation and stress to guarantee traction-free boundary conditions without the necessity of shear correction factors. The proposed two-node beam element has three degrees of freedom per node, and the inter-element continuity is retained using both C1 and C0 continuities for kinematics variables. In addition, the mechanical properties of the (BDFG) beam vary gradually and smoothly in both the in-plane and out-of-plane beam’s directions according to an exponential power-law distribution. The highly elevated performance of the developed model is shown by comparing it to conceptual frameworks and solution procedures. Detailed numerical investigations are also conducted to examine the impact of boundary conditions, the bi-directional gradient indices, and the slenderness ratio on the free vibration response of BDFG beams. The suggested finite element beam model is an excellent potential tool for the design and the mechanical behavior estimation of BDFG structures. © 2024 Techno-Press, Ltd.

关键词： Boundary conditions

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