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Exploring Factors Influencing Energy Consumption Behavior Among Higher Education Students Using Deep learning 1

Exploring Factors Influencing Energy Consumption Behavior Am...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Mishra, Amit Kumar Singh, Jagendra Yadav, Alok Kumar Diwakar, Manoj Singh, Prabhishek Lamba, Sumita Graphic Era Hill University Department of CSE Dehradun India School of Computer Science Engineering & Technology Bennett University Greater Noida India Naraina Vidya Peeth Engineering and Management Institute Department of Computer Science and Engineering Kanpur India Graphic Era Deemed to be University Department of CSE Dehradun India School of Computing DIT University Mussoorie India

ISBN: (纸本)9798350353648

This research examines the dynamics of university students' energy usage behaviour using Internet of Things (IoT) technologies. The assignment meticulously positions a range of sensors in labs and lecture rooms and combines them with gadget learning algorithms to be able to maximize energy consumption. For a month, the Internet of Things sends actual-time notifications to students approximately the need for careful strength management. Before the implementation observations provide a comparative evaluation of student behavior and draw interest to the subtle behavioural adjustments as a result of psychological shifts. Machine studying fashions are built to forecast destiny energy use by way of using the information generated by way of those processes. These fashions include Decision Trees, Random Forests, Support Vector Machines, and Artificial Neural Networks. The consequences display a noteworthy decrease in strength use with the IoT machine in region, highlighting the significance of technological interventions in encouraging environmentally pleasant behaviour. From an academic angle, this advances our expertise of strength behaviour and gives useful information to educational establishments trying to find creative techniques to implementing responsible electricity control. © 2024 IEEE.

关键词： Students

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Smart Home Secure Network using Decentralized Infrastructure and Machine learning Techniques

Smart Home Secure Network using Decentralized Infrastructure...

引用

2024 Antenna Design and Measurement international conference, ADMInC 2024

作者： Yassin-Kazem, Habib Attarzadeh, Imam Central Tehran Branch Islamic Azad University Faculty of Engineering Department of Computer Engineering Tehran Iran

ISBN: (纸本)9798350390568

Security vulnerabilities in telecommunications networks have become an important issue due to the significant growth of gadgets for smart homes. and its connectivity via the Internet of Things (IoT) This research supports the smart home network learning engine using secure communications based on the blockchain system and evaluation layers based on cloud systems for determine their priorities and classify them into three kinds of coefficients (T): Avoid T. Smart T and Mod T. The learning engine uses a neural network to train and classify these types and the blockchain helps layer make more effective decisions. Key contributions of this study include implementing a secured blockchain layers for user authenticating and creating a ledger for the communication networks. Besides, using of a cloud-depend datas assessment layers, the development of the SI-depend teaching algorithm, and the complementarity of a neural engine for accurate classification are highlighted. The suggested algorithm excels the sequentially deep learning machine (RTS-DELM), data fusion techs., and IoT AL. technologies in affording electronic information engineering and improvement strategy analysis. This is achieved by reducing computational complication, false authentication rates, and quality parameters, ensuring a safty and effective smart home communication network that improves human life style. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Transfer learning-based Models for Sign- Language Recognition 15

Comparison of Transfer learning-based Models for Sign- Langu...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sharma, Shivani Sharma, Sukesha University Institute of Engineering and Technology Panjab University Department of Information Technology Chandigarh160014 India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370249

People with disabilities includes deaf and hard of hearing cannot avail the benefits of education, proper health and find difficulty to communicate with others. They need sign language interpreter to communicate. Sign Language Recognition is important because it helps millions of people who are deaf or hard of hearing to communicate with each other. As sign language is different for different regions and countries. Sign Language Recognition is a complex field with many research opportunities, driven by advancements in artificial intelligence. The taxonomy of sign language recognition includes applications, computational resources, features, input modalities, datasets, and classification techniques. Many authors proposed various techniques in the previous years for sign language recognition which are based on the machine learning and deep learning. In order to improve the precision and effectiveness of sign language recognition systems, this research investigates the use of transfer learning approach. It includes, sign language recognition system employs various transfer learning models like ResNet50, ResNet50v2 and VGG16 for sign language characters including letter and numbers' recognition. The results show VGG16 outperformed with accuracy of 97.7 percent. The goal of this research is to achieve the high accuracy in less training time followed by fewer epochs and handle images of different size with the use of transfer learning. © 2024 IEEE.

关键词： Transfer learning

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A Grading Model for Jasmine Flowers Using Deep learning and Quantum Machine learning 4

A Grading Model for Jasmine Flowers Using Deep Learning and ...

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4th international conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Pratiksha Cauvery, K.P. Thrisha, R. Harshitha, T. Panigrahi, Suman Thangavel, Senthil Kumar Somasundaram, K. Boopathy, C.P. Amrita School of Physical Sciences Department of Mathematics Tamil Nadu Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Coimbatore India Amrita School of Engineering Department of Elelctical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798331523923

The flowers of Jasmine hold immense economic importance in the agricultural sector and the perfume industry, as the value and satisfaction of the market, are heavily influenced by the quality of these flowers. The already very labor-intensive, subjective, and inefficient manual method of grading plants based on their physical and aromatic characteristics is unsuitable on a large scale. This paper handles these limitations by introducing automation in the grading process using Deep learning and Quantum Machine learning. The conceptual methodology mandates automatic flower placement and high-resolution imagery with a conveyor belt. The deep learning model, exactly a type of Convolutional Neural Network (CNN), would explain freshness in terms of visual features like color, texture, structural strength, and vice versa. Additionally, quantum-inspired algorithms have been employed to enhance the computation and performance of the model. From the analysis, a decision-making module classifies entrant flowers into fresh and otherwise, thereby sending them to their respective places, where all packing or disposal could be achieved. This DL-QML hybrid approach points out the exemplary high accuracy, scalability, and efficiency in jasmine grading and hence minimizes manual intervention in quality control assembly lines. The findings provide the card of the feasibility of joining QML in addressing computational challenges of agro-product quality assessment, opening avenues in application far broader than floriculture. © 2025 IEEE.

关键词： Decision making

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A Novel Phishing Attack Detection System Based on Web Platforms by Using Deep learning Methodology

A Novel Phishing Attack Detection System Based on Web Platfo...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Gandhi, Tella Porkodi, M. Sheela Shantha Kumari, P.K. Vijaya Vardan Reddy, S.P. Giri, Jayant Samhan, Ahmad Abdelhafiz Ali Mlr Institute of Technology Dept of Csit Dundigal Hyderabad India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Chennai India R.M.K. Engineering College Department of EC-ACT Kavaraipettai Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Software Engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Global economic expansion facilitated by technology has led to an evolution in phishing attacks in recent years. An uptick in phishing and other sophisticated online attacks, together with an uptick in impersonation frauds, has contributed to a general uptick in fraudulent activity losses this year. To safeguard users' online activities, a more effective phishing detection mechanism is necessary, since the worldwide impact of these attacks will only grow. Using the URL and website content such photos, text, and frames, this study aimed to build a deep learning-based phishing detection system to fill this demand. When it comes to image and natural language processing, deep learning methods work wonders. Binary learning with Phishing Detection Strategy (BLPDS) is a novel approach to efficiently detect phishing attacks introduced in this paper. To assess its efficacy, it is cross-validated with the conventional Long Short-Term Memory (LSTM) method. Applying the BLPDS to big datasets allowed for a thorough experimental evaluation and comparison of its efficacy in detecting phishing web pages and attacks. The precise proof demonstrating the efficacy of the suggested system is shown in the result section. © 2024 IEEE.

关键词： Phishing

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A Categorization and Evaluation on Industry Specific Machine learning Models and Their Acceptability 7

A Categorization and Evaluation on Industry Specific Machine...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Goel, Om Bhutia, Rudramani Kavuri, Swethasri Salunkhe, Vishwasrao Gangu, Krishna Agrawal, Shashwat Abes Engineering College Uttar Pradesh Ghaziabad India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India Andhra Pradesh Vijayawada India Maharashtra Pune India Telangana Hyderabad India Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350350067

Machine learning (ML) is a powerful technology that is revolutionizing several industries, including medicine, finance, and transportation. This research offers a thorough examination and assessment of machine learning methods used in various fields, with a special emphasis on their effectiveness and versatility in tackling industry-specific obstacles. ML models including XGBoost, Random Forest, and Gradient Boosting have shown outstanding performance in healthcare by accurately predicting cardiovascular illnesses and diabetes. Among these models, XGBoost has achieved the greatest accuracy and recall. Within the realm of finance, a combination of algorithms such as XGBoost and Random Forest proved to be more effective than conventional models in detecting fraudulent activities. This ensemble method demonstrated increased precision and a decline in the frequency of false positives. The Temporal Graph Convolutional Network (T-GCN) has been identified as the most efficient model in the transportation industry for forecasting Norwegian air passenger traffic. It surpasses other models concerning error and accuracy metrics. This work demonstrates how machine learning may be used to enhance decision-making and highlights the importance of selecting the right model depending on application needs, efficiency, and prediction accuracy in several industries. The findings provide helpful information on the effectiveness of various machine learning models and their applicability to actual situations, therefore facilitating future progress and utilization of machine learning technology. © 2024 IEEE.

关键词： Decision trees

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***: A COMPOSABLE ENERGY-BASED MODELING SYSTEM FOR MECHATRONICS AND SYSTEMS ANALYSIS IN EDUCATION 15

***: A COMPOSABLE ENERGY-BASED MODELING SYSTEM FOR MECHATRON...

引用

15th international conference on Bond Graph Modeling and Simulation, ICBGM 2024

作者： Farmer, Carson Medina, Hector School of Engineering Liberty University 1971 University Blvd LynchburgVA United States

Multi-domain engineering modeling and simulation is becoming more relevant and needed to undergraduate engineering education. The sole idea of synergistically teaching/learning multiple engineering domains-using energy as the main learning transaction unit-could simplify undergraduate engineering curricula. However, there is a need to develop more technological tools to assist this endeavor. Here we introduce ***, a modern approach to the programmatic implementation of bond graphs. The software package provides a simple interface for constructing bond graphs, deriving equations, and building hierarchical systems. The package facilitates the implementation of both linear and non-linear models, state-space form reduction, and transfer function derivation. By leveraging a high-performance computer algebra system, the reduction of large (non)linear bond graph systems is possible with minimal input from the user. Finally, an implementation of the package into I3-LIB, a proof-of-concept by Vagus, LLC, demonstrates the effectiveness of the package in bond graph education by providing an interactive modeling and simulation experience to students. © 2024 Society for Modeling & Simulation international (SCS)

关键词： Hierarchical systems

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Zero-Shot and Few-Shot learning for Telugu News Classification: A Large Language Model Approach 15

Zero-Shot and Few-Shot Learning for Telugu News Classificati...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Boyina, Kamal Reddy, Gujja Manaswi Akshita, Gunturi Nair, Priyanka C. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Telugu news classification involves categorizing news articles written in the Telugu language into five categories: business, editorial, entertainment, nation, and sport. Zero-shot classification enables categorization into unseen classes without any prior training on those specific classes. Few-shot classification generalizes with minimal training data by leveraging a limited number of labeled examples per class. This work leverages Large Language Models (LLMs) such as mBERT, Indic-BERT, XLM-Roberta, and Flan-T5 Large for zero-shot classification. For few-shot classification, models like Flan-T5 and BART are used in 1 -shot, 3 -shot, and 5 -shot scenarios. Traditional machine learning models including Support Vector Machines (SVM), Naive Bayes, AdaBoost, Random Forest, and Logistic Regression are also explored for comparison. Prompt engineering is employed with various prompts to enhance zero-shot classification performance. Among the LLMs, mBERT achieves the highest F1-score of 0.58 in zero-shot classification. For few-shot classification, Flan-T5 achieved an F1-score of 0. 2 3. © 2024 IEEE.

关键词： Few-shot Flan-T5 Large Language Models mBERT Prompt engineering Text Classification Zero-shot

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Artificial Intelligence-based Service Chains Scheduling for Medical Emergency in Healthcare 7

Artificial Intelligence-based Service Chains Scheduling for ...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Soni, Mukesh Rusho, Maher Ali Byeon, Haewon Alghamdi, Azzah Hannan, Shaikh Abdul Dave, Parth Rameshchandra Dr. D. Y. Patil School of Science & Technology Tathawade Dr. D. Y. Patil Vidyapeeth Marashtra Pune India University of Colorado Department of Lockheed Martin Engineering Management BoulderCO80309 United States Inje University Department of Ai and Software & Medical Big Data Research Centre Gimhae50834 Korea Republic of Imam Abdalrhman Bin Faisal University College of Computer Science and Information Technology Computer Information Systems Department Khobar Saudi Arabia AlBaha University Faculty of Computing and Information AlBaha Saudi Arabia L. D. College of Engineering Department of Computer Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350350067

This study analyses the weekly scheduling challenge of health and medical information network service function chains to meet medical emergency categorization and grading needs. Emergency services are divided into four sorts by urgency and service area, with priorities and weights. The health and medical information network architecture, service model, and operation method are built accordingly. To reduce weighted completion time, a service function chain scheduling model is created. The function chain scheduling scheme is solved by matching game and Q -learning reinforcement learning algorithms for different issue scales. Finally, simulation studies demonstrate that this approach can accomplish graded medical emergency service, build health and medical information networks, and progress smart healthcare. © 2024 IEEE.

关键词： Electronic health record

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE international conference on Advances in Signal Processing, Power, Communication, and computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-Computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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