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Enhancing the Efficacy Of Virtual Study Spaces In the Age Of Online learning 4

Enhancing The Efficacy Of Virtual Study Spaces In The Age Of...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Panda, Akshyat Kumar Dash, Anushka Johari, Kali Das, Swati Dey, Raghunath Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

In response to the evolving technology, the field of education and remote learning is undergoing a significant shift to an online mode, connecting students and educators worldwide at the press of a button. A novel virtual study room platform has been developed to efficiently handle this shift and eliminate the existing barriers. After thorough research and reviewing feedback from students during the COVID19 pandemic, the platform aims to include seamless video-audio interface fostering a sense of community, collaborative document sharing, and robust security protocols. the Pomodoro technique for effective study sessions, dedicated and secure private study rooms with identity and password systems, document and resource sharing, and real-time video and audio communication are just a few of the crucial features that this platform incorporates to overcome existing limitations. With more ideas for development, the platform has a promising future to bring about a change in the era of online learning by providing a more engaging, efficient, and collaborative space for academic interaction. © 2024 IEEE.

关键词： Students

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Exploring Advanced Approaches: A Comprehensive Analysis of Machine learning and Deep Neural Networks in Spectrum Sensing Applications 4

Exploring Advanced Approaches: A Comprehensive Analysis of M...

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4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Karthiga, G. Saravanan, K. Amc Engineering College Dept. of Computer Science and Engineering Karnataka Bengaluru India New Horizon College of Engineering Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529635

the increasing demand for wireless communication resources has intensified the need for effective spectrum sensing techniques to alleviate the scarcity of available spectral bands. this survey paper presents an indepth exploration and comparative analysis of various spectrum sensing methodologies, focusing on Machine learning (ML) and Deep Neural Network (DNN) approaches. the study delineates the landscape of traditional spectrum sensing techniques, encompassing energy detection, matched filtering, cyclo-stationary feature detection, and cooperative spectrum sensing. this paper explains sensing using machine learning-based such as Support Vector Machines (SVM), Random Forest, Neural Networks, and Hidden Markov Models (HMM) are systematically elucidated. Moreover, the paper meticulously scrutinizes the advancements in deep learning-based spectrum sensing, encapsulating the utilization of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Reinforcement learning (DRL) methodologies. the discussion encompasses their applications, advantages, and limitations in the context of spectrum sensing. © 2024 IEEE.

关键词： Hidden Markov models

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4th International conference on Data, Engineering, and Applications, IDEA 2022

4th International Conference on Data, Engineering, and Appli...

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4th International conference on Data, Engineering, and Applications, IDEA 2022

ISBN: (纸本)9789819724505

the proceedings contain 74 papers. the special focus in this conference is on Data, Engineering, and Applications. the topics include: Using OpenNLP and GraalVM to Detect Sentences in Kubernetes While Comparing Helidon and Spring Boot’s Metrics;an Efficient Hybrid Model to Summarize the Text Using Transfer learning;automatic Detection of Learner’s learning Style;construction of an intelligent Knowledge-Based System Using Transformer Model;machine learning-Based Disease Diagnosis Using Body Signals: A Review;finite Difference and Finite Volume 1D Steady-State Heat Conduction Model for Machine learning Algorithms;Sign Language Detection through PCANet and SVM;A Novel Surface Roughness Estimation and optimization Model for Turning Process Using RSM-JAYA Method;effective Prediction of Coronary Heart Disease Using Hybrid Machine learning;feature Extraction Using Levy Distribution-Based Salp Swarm Algorithm;plant Disease Detection Using Machine learning Approaches: A Review;copy–Move Forgery Detection Algorithm: A Machine learning-Based Approach to Detect Image Forgery;a Machine learning-Based Approach to Combat Hate Speech on Social Media;Prediction of SARS-COVID-19 Based on Transfer Machine learning Techniques Using Lungs CT Scan Images;online Document Identification and Verification Using Machine learning Model;Mitigating Partial Shading Condition in PV System for MPPT Using Evolutionary Algorithms;road Safety Modeling: Safe Road for All;AI-Enabled Road Health Monitoring System for Smart Cities;multi-objective Biofilm Algorithm to Resolve optimization Problems;comparative Analysis of Fake News Identification Using Machine learning Methods;a Review of Pre-processing Techniques for Weed-Plant Detection and Classification in Precision Agriculture;utilizing a Finger Vein in Biometric Authentication Mechanism;local Binary Patterns-Based Retinal Disease Screening.

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Multi-Agent Reinforcement learning for Energy Management in Community Energy Trading 4

Multi-Agent Reinforcement Learning for Energy Management in ...

引用

4th Power System and Green Energy conference (PSGEC)

作者： Liang, Zeting Zheng, Jiehui Li, Zhigang South China Univ Technol Sch Elect Power Engn Guangzhou Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9798350365573;9798350365580

Considering the differentiated characteristics of prosumers in new power systems, the high importance of privacy in energy trading and the limitations of traditional modelbased optimization methods within multiple uncertainties, this paper proposes a multi-agent reinforcement learning method with differentiated characteristics and privacy preservation for energy management. Firstly, the differentiated characteristics of prosumer are analyzed and corresponding typical prosumer models are established. Secondly, based on the community market structure, a community energy trading model based on the midmarket rate pricing is constructed. Finally, taking market benefits and operating costs as optimization objectives, the energy trading optimization of prosumers participating in community energy trading is constructed into a partially observable Markov decision process. Aiming at the non-stationary problem of multi-agent environment, this paper proposes to approximate the central Q function of the soft actor-critic algorithm by the meanfield approximation mechanism. the proposed algorithm is then employed to obtain the prosumers' energy management decision. Results of case study show that the proposed algorithm has outstanding advantages in aspects of convergence, efficiency and economy in energy management within community market considering differentiated characteristics and privacy preservation.

关键词： community energy market differentiated characteristics privacy preservation reinforcement learning soft actor-critic

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Crop Recommendation System using Supervised learning Techniques 4

Crop Recommendation System using Supervised Learning Techniq...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Disha, M. Dash, Satya Ranjan Mohapatra, Debasish Jena, Damodar Barra, Paola Kiit University School of Computer Applications Bhubaneswar India KIIT DU Kiit School of Rural Management Bhubaneswar India University of Naples Department of Science and Technology Italy

ISBN: (纸本)9798350349856

Agriculture plays a pivotal role in the economic development of a country. Different agro-climatic conditions are suitable for different crops. Farmers face a lot of difficulty in deciding which crop to grow. Advancing technology can be very fruitful in deciding which crops to grow for maximum production and return on investment. Multiple research studies have been performed to develop a system that would recommend crops based on the conditions available. In this paper, three different machine learning models have been used to develop a crop recommendation system based on the different parameters. the three models are compared to find the best solution. © 2024 IEEE.

关键词： Crops

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A Comparative Study of Deep learning Techniques for Network Intrusion Detection 4

A Comparative Study of Deep Learning Techniques for Network ...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Pant, Pranav Kumar, Aniket Vashishtha, Lalit Kumar Dash, Subhasis Ray, Niranjan Kumar Sahu, Santosh Kumar Kiit University School of Computer Engineering Bhubaneswar India Oil and Natural Gas Coorporation Ltd Geodata Processing and Interpretation Center Dehradun India

ISBN: (纸本)9798350349856

In an era dominated by interconnected technologies, the persistent threat of intrusion attacks looms ominously, leaving individuals and organizations vulnerable to devastating consequences. the insidious nature of these attacks not only compromises sensitive information but also jeopardizes the very fabric of digital trust. As the frequency and sophistication of intrusions escalate, there is an urgent need for robust defenses to safeguard against these malicious incursions. this paper addresses this pressing concern by delving into the realm of deep learning, a cutting-edge field that holds the promise of fortifying our digital fortresses. We present a comprehensive analysis of various deep learning approaches, rigorously tested on two benchmark datasets: UNSW-NB15 and 5G-NIDD. these datasets serve as battlegrounds where our models are trained and tested to discern their efficacy in the critical task of intrusion detection. © 2024 IEEE.

关键词： Intrusion detection

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A Transfer learning and Feature Ranking-based Feature Extraction Approach for Enhancing Skin Lesion Classification 4

A Transfer Learning and Feature Ranking-based Feature Extrac...

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4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Mohanty, Niharika Pradhan, Manaswini Mallick, Pradeep Kumar Routray, Ashanta Ranjan Fakir Mohan University Department of Information and Communication Technology Odisha Balasore India Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

this study investigates skin lesion classification through feature fusion, focusing on transfer learning-based extraction for improved model discernment. Utilizing VGG16, ResNet, and EfficientNet B0, the research ranks features using methods like model weights, RFE, correlation-based techniques, LASSO regression, gradient boosting, and variance thresholding. the approach aims to enhance diagnostic precision by combining diverse features essential for accurate skin lesion classification. Assessing HAM 10000 and BCN 20000 datasets, the study evaluates the impact of ranking on model performance. Results consistently demonstrate that ranked feature sets outperform initial sets across classifiers (KNN, SVM, CNN) for both datasets. Notably, CNN excels with ranked feature sets from RFE applied to fused features from transfer learning networks. © 2024 IEEE.

关键词： Extraction

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Use of Machine learning in Object Detection for Visually Impaired Person 4

Use of Machine Learning in Object Detection for Visually Imp...

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Munia, Tahmina Haque Taher, Kazi Abu Bangladesh University of Professionals Dhaka Department of Information and Communication Technology Bangladesh

ISBN: (纸本)9798350349900

thisreport presents an object detection model for visually impaired persons for object detection, face recognition, navigation and distance measurement. the system model uses a webcam to captures visual images. the images are compressed to a resolution of 300×300 pixels. the MobilenetV3 model is used for object detection which then are classified into human and non-human types. A face detection algorithm is used to recognize human faces. the distance measurement process uses the focal length of the camera and a calibration pattern to calculate the distance of the objects. the proposed object detection model achieved an impressive performance on the test dataset with an average precision of 92.5%, achieving an accuracy of more than 98% for human detection and approximately 91.7% for other objects. the proposed integrated model's high accuracy and precision make it a promising solution for the visually impaired people © 2024 IEEE.

关键词： Adversarial machine learning

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CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Communication 4

CleanComm: A Hybrid ACO-WOA-MOA Model for Sustainable Commun...

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4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Aggarwal, Vaishali Goel, Kashvi Gupta, Ishika Ajmani, Prerna Vivekananda Institute of Professional Studies School of Information Technology Technical Campus Delhi India

ISBN: (纸本)9798331518981

Wireless sensor networks (WSNs) and their applications are on an increasing trend owing to the users' demands with interest in environmental activities, intelligent cities, and medical assistance. However, because of the inefficiency and limitations of wireless sensor networks, it is still very difficult to optimize the network parameters and performance (energy consumption, area coverage and reliable information transfer). In this paper optimization algorithms including Ant Colony optimization (ACO), Moth Flame optimization (MFO) and Whale optimization Algorithm (WOA) to enhance the effectiveness of wireless sensor systems are presented. In this approach, ACO captures and explores functional properties, MFO coordinates the optimization and exploration processes, and WOA concentrates on global exploration. Wide-ranging simulations demonstrate that the recommended approach is more effective than the existing approaches in enhancing energy efficiency, network lifetime and effectiveness of packet delivery. the findings in this paper indicate that the proposed hybrid algorithms support the various requirements of the WSN, thus promoting development of networks that are secure and dependable. © 2024 IEEE.

关键词： Ant colony optimization

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Performance Analysis of Gravitational Search Algorithm During Parametric optimization of Machining Processes 4th

Performance Analysis of Gravitational Search Algorithm Durin...

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4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Aditya, Nikhil Mahapatra, Siba Sankar Natl Inst Technol Dept Mech Engn Rourkela Odisha India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

optimization of machining parameters is usually carried out to enhance the performance of the machining processes. Generally, the design of experiments approach and multi-response optimization methods are applied to obtain the best parametric setting of a process. However, these methods result in local optimal solutions because the search is limited to discrete points. therefore, classical optimization methods and metaheuristics are extensively used to obtain the optimized machining condition. Cuckoo search (CS), teaching learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA) algorithms have proved their efficiency for optimizing machining processes. Despite outperforming evolutionary and swarm intelligence techniques on unconstrained benchmark functions, few studies have used a gravitational search algorithm (GSA) to obtain the best parametric condition in a machining process. therefore, the present study attempts to quantify the performance of GSA and chaotic GSA (CGSA) while determining the best parameters for a machining process. the present study adopts different cost functions involved in turning, wire electrical discharge machining (WEDM), and plasma-enhanced chemical vapor deposition (PECVD). A comparison of results obtained with TLBO and PSO indicates the superiority of TLBO by maintaining better mean and standard deviation values. However, CGSA performs significantly better than GSA and PSO. Friedman's test ranks TLBO as the best algorithm, followed by CGSA, PSO, and GSA.

关键词： GSA TLBO PSO Parametric optimization

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