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2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023

作者： Rajak, Pappu Kumar Tigga, Alka Divya Talukdar, Veera Praveenkumar, S. Namdev, Arpit Gupta, Ankur Doranda College Dept. of Commerce Jharkhand Ranchi India D y Patil International University Dept. of Computer Science MaharashtraAkurdi Pune India Madurai Kamaraj University Centre for Tourism and Hotel Management TN Madurai India University Institute of Technology Rgpv Dept. of It MP Bhopal India Vaish College of Engineering Dept. of Cse Haryana Rohtak India

ISBN: (纸本)9798350380880

In the rapidly evolving realm of e-commerce, the equilibrium between demand and supply is a linchpin for operational success and financial viability. This abstract encapsulates the core challenge of optimizing decision-making processes within e-commerce applications to navigate the intricate landscape of supply and demand dynamics. With consumer preferences in a state of constant flux and market trends evolving at an unprecedented pace, the challenge lies in comprehending and responding adept.y to these fluctuations. The primary objective is to devise dynamic, analytics-driven decision-making frameworks that empower e-commerce platforms to optimize their demand and supply dynamics, simultaneously ensuring customer satisfaction and sustainable business growth. The stakeholders most affected by the efficacy of these strategies include e-commerce managers, supply chain professionals, and consumers, further emphasizing the urgency and relevance of this issue in the contemporary e-commerce landscape. In the current study, an Analysis of Variance (ANOVA) approach has been used to examine the potential influence of employment on high, moderate, and low levels of demand and supply. ANOVA, or analysis of variance, is a statistical method often used in workforce diversity research. Its purpose is to evaluate if there are statistically significant disparities in a certain outcome variable among various employee groups, as determined by their diversity characteristics. © 2023 IEEE.

关键词： Decision making

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Joint attacks and accrual in argumentation frameworks

Journal of Applied Logics

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Journal of Applied Logics 2021年第6期8卷 1437-1501页

作者： Bikakis, Antonis Cohen, Andrea Dvorák, Wolfgang Flouris, Giorgos Parsons, Simon United Kingdom Institute for Computer Science and Engineering CONICET-UNS Dept. of Computer Science and Engineering Universidad Nacional del Sur Bahía Blanca Argentina Institute of Logic and Computation TU Wien Austria Greece University of Lincoln United Kingdom

While modelling arguments, it is often useful to represent "joint attacks", i.e., cases where multiple arguments jointly attack another (note that this is different from the case where multiple arguments attack another in isolation). Based on this remark, the notion of joint attacks has been proposed as a useful extension of classical Abstract Argumentation Frameworks, and has been shown to constitute a genuine extension in terms of expressive power. In this chapter, we review various works considering the notion of joint attacks from various perspectives, including abstract and structured frameworks. Moreover, we present results detailing the relation among frameworks with joint attacks and classical argumentation frameworks, computational aspects, and applications of joint attacks. Last but not least, we propose a roadmap for future research on the subject, identifying gaps in current research and important research directions. © 2021, College Publications. All rights reserved.

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I See What You're Hearing: Facilitating The Effect of Environment on Perceived Emotion While Teleconferencing

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Proceedings of the ACM on Human-computer Interaction 2023年第1 CSCW期7卷 1-15页

作者： Marino, David Henry, Max Fortin, Pascal E. Bhayana, Rachit Cooperstock, Jeremy McGill University Center for Intelligent Machines MontrealQCH3A 0G4 Canada McGill University Dept. of Electrical and Computer Engineering MontrealQCH3A 0G4 Canada Indraprastha Institute of Information Technology Dept. of Human Centered Design New Delhi Delhi110020 India

Our perception of emotion is highly contextual. Changes in the environment can affect our narrative framing, and thus augment our emotional perception of interlocutors. User environments are typically heavily suppressed due to the technical limitations of commercial videoconferencing platforms. As a result, there is often a lack of contextual awareness while participating in a video call, and this affects how we perceive the emotions of conversants. We present a videoconferencing module that visualizes the user's aural environment to enhance awareness between interlocutors. The system visualizes environmental sound based on its semantic and acoustic properties. We found that our visualization system was about 50% effective at eliciting emotional perceptions in users that was similar to the response elicited by environmental sound it *** contributed system provides a unique approach to facilitate ambient awareness on an implicit emotional level in situations where multimodal environmental context is suppressed. © 2023 ACM.

关键词： Visualization

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CPP-OTPO: A Cognitive Multi-UAV System for Adaptive Path and Trajectory Optimization

CPP-OTPO: A Cognitive Multi-UAV System for Adaptive Path and...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Gunasekaran Raja Selvam Essaky Surya Raj M Jawaharlal Nehru N Christ Joe Kapal Dev Waleed Ejaz Department of Computer Technology NGNLab Anna University Chennai India Dept. of Computer Science Munster Technological University Ireland Department of Electrical and Computer Engineering Lakehead University ON Canada

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Unmanned Aerial Vehicles (UAVs) have significantly transformed various Search and Rescue (SAR) operations. Effective resource deployment is still difficult in modern SAR missions, especially in complex and unexpected circumstances. Conventional techniques for multi-UAV systems for SAR operations frequently have difficulties responding to changing conditions. This research proposes a unique method of combining 6G communication technology and Deep Reinforcement Learning (DRL) to improve the cognitive capacities of multi-UAV systems for cooperative coverage to enhance autonomous decision-making in challenging environments. The proposed Coverage Path Planning-Online Time-based Policy Optimization (CPP-OTPO) system utilizes DRL to imbue a multi-UAV framework capable of enabling adaptive decision-making based on realtime observations. The integration of 6G communication further enhances connectivity among multi-UAV systems. The system incorporates boundary search techniques in UAV path planning to achieve optimized CPP, emphasizing resilience against navigational uncertainties. Additionally, the proposed OTPO improves the effectiveness of the target search and increases the possibility of detecting targets by guiding UAVs to change their search policies from exploiting to exploring when required. Empirical evaluations demonstrate a remarkable 92.2% operational time efficiency compared to traditional algorithms.

关键词： 6G mobile communication Adaptive systems Uncertainty Navigation Decision making Autonomous aerial vehicles Deep reinforcement learning

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A Literature Review of Machine Learning Techniques for Recommender System In E-Commerce 5

A Literature Review of Machine Learning Techniques for Recom...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ajibade, Samuel-Soma M. Jasser, Muhammed Basheer Hoong, Angela Lee Siew Culpable, Rex V. Quirante, Celestino A. Dacillo, Arvie L. Adetunla, Adedotun O. Akintoye, Kayode Akinlekan Sunway University School of Engineering and Technology Department of Data Science and Artificial Intelligence No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia Selangor Malaysia Cebu Technological University College of Technology Cebu Moalboal Philippines Afe Babalola University Dept. of Mechanical and Mechatronics Engineering Ekiti Ado Nigeria University of Johannesburg Dept. of Mechanical Engineering Science Johannesburg South Africa The Federal Polytechnic Department of Computer Science Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9798331542559

The swift expansion of e-commerce has prompted the creation of advanced recommender systems to augment user experience and boost sales. Machine learning techniques have become fundamental for the development of these systems, allowing firms to provide tailored product recommendations based on customer preferences and habits. This research presents a thorough literature assessment of machine learning methodologies utilized in recommender systems, emphasizing their predictive efficacy in the e-commerce sector. We analyze essential methodologies, including collaborative filtering, content-based filtering, hybrid models, and sophisticated techniques such as deep learning. Through the examination of 38 research in the domain, we elucidate the limitations, advantages, and possibilities of machine learning algorithms in delivering precise and scalable suggestions. Our research indicates that hybrid models, which integrate many recommendation strategies, demonstrate significant potential in addressing challenges like the cold start problem and data sparsity. The report delineates prospective research avenues, underscoring the necessity for continued innovation in the integration of machine learning into recommender systems to improve accuracy and customer happiness in e-commerce settings. © 2024 IEEE.

关键词： Marketplaces

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Face Recognition Using openCV- use case on student attendance system

Face Recognition Using openCV- use case on student attendanc...

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International Conference on Computing and Networking technology (ICCNT)

作者： Anjan Kumar Kamalapur Begari Srikanth Asari Shivadeep Yadav Bachhanaboni Yashwanth Vivek Duraivelu Dept.of Computer Science Engineering B V Raju Institute of Technology Narsapur Telangana

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This is a modern approach to student attendance management using face recognition technology, powered by advanced techniques including Convolutional Neural Network (CNN) and Haar Cascade frontalface default algorithm. By leveraging OpenCV and these cutting-edge face recognition methods, the system offers a reliable and efficient solution for tracking attendance in educational institutions. Real-time face detection and recognition, support for multiple face recognition, and smooth interaction with current attendance management systems are some of the key characteristics. Additionally, the system incorporates deep learning algorithms to enhance its accuracy and robustness in identifying students’ faces. Through practical implementation, the project demonstrates the effectiveness of using OpenCV and deep learning for automated student attendance management. By providing accurate and efficient attendance tracking, while improving operational efficiency in educational settings, this innovative solution marks a significant advancement in modernizing attendance management practices.

关键词： Deep learning Privacy Accuracy Face recognition Neural networks Real-time systems Robustness Convolutional neural networks Face detection Character recognition

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Real-Time Anomaly Detection in 5G Networks Through Edge Computing 3

Real-Time Anomaly Detection in 5G Networks Through Edge Comp...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Shaik, Riaz Raju, Dara Behera, Prakash Chandra Changala, Ravindra Mary, S. Suma Christal Balakumar, A. Kl University Department of Cse Andhra Pradesh Vaddeswaram India Vignana Bharathi Institute and Technology Dept. of Computer Science & Engineering Aushapur Ghatkesar India St. Claret College Department of Science Bangalore India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K. Ramakrishnan College of Engineering Department of Ece samayapuram Trichy India

ISBN: (纸本)9798350361186

Strong anomaly detection techniques are becoming more and more necessary as 5G networks develop in order to maintain network performance, security, and dependability. This study leverages the capabilities of Mobile Edge Computing (MEC) to present a novel method for anomaly detection in 5G networks. By processing data closer to the network edge, the integration of MEC offers an efficient and decentralized architecture that lowers latency and improves real-time detection capabilities. The distributed module takes advantage of its close proximity to network devices by using sophisticated algorithms for anomaly detection, which are implemented at the mobile edge. The system can quickly detect abnormalities from typical network activity by utilizing capabilities including Flow Collection, Anomaly Symptom Detection, and Network Anomaly Detection. The distributed module provides anomalous information to the centralized decision-making module for thorough examination. It takes into account variables like resource use and network traffic and integrates this data with metrics gathered from monitoring modules. Because of its adaptive characteristics, the system may expand anomaly detection components, enhance detection functions, and modify virtualized resources in response to shifting network circumstances. The evaluation findings reveal that the suggested anomaly detection method performs well in 5G networks, with decreased false positives, increased responsiveness, and better flexibility to changing network conditions. Using MEC not only makes anomaly detection more effective, but it also fits in with the 5G design, which makes it a viable option for protecting the upcoming generation of communication networks. It obtains 95% accuracy in classification. The suggested approach has proven to be resilient in handling security issues by producing outcomes that are either comparable to or better than those attained by other techniques that have been previously presented in the s

关键词： 5G mobile communication systems

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Additive Decomposition of Bent Functions

Additive Decomposition of Bent Functions

引用

International Symposium on Multiple-Valued Logic

作者： Claudio Moraga Radomir S. Stanković Milena Stanković Faculty of Computer Science Technical University of Dortmund Germany Mathematical Institute S.A.S.A. Belgrade Serbia Dept. Computer Science Faculty of Electronic Engineering Niš Serbia

ISBN: (数字)9798331507442

ISBN: (纸本)9798331507459

An additive decomposition of multiple-valued bent functions is studied. It is shown that multiple-valued bent functions may be decomposed with respect to the arithmetic addition, the modulo p addition and the tensor sum of value vectors of p-valued functions. The number of some possible decompositions of bent functions is determined. Examples of additive decomposition of p-valued bent functions for p ∈ {3, 4, 5} are given. It is shown that under structural conditions there are functions which are not bent, but may have close related decompositions.

关键词： Additives Tensors Vectors Logic Arithmetic

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Intelligent Face Detection and Recognition System

Intelligent Face Detection and Recognition System

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2021 International Conference on Intelligent Technologies, CONIT 2021

作者： Sikder, Juel Chakma, Ratna Chakma, Rana Jyoti Das, Utpol Kanti Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh

ISBN: (纸本)9781728185835

Face detection applications currently can use machine learning and equations for better identification of human faces in an image or video. In several types of applications such as recognition of face application that is processing in method of biometric for checking the human's validation which is related to facial information and different types of samples utilizing face detection system. This paper of presentation shows that system of detection and recognition in face by Viola-Jones process add together Haar Cascade and Principal Component Analysis (PCA) of technique which is proposed for gaining better accuracy level in detection of facial portions and recognition of faces. The findings show that the proposed system has performed better compared with other conventional face detection and recognition systems. The investigation has been done in a MATLAB environment on real images of the Faces94, Faces95, Faces96, and grimace databases. © 2021 IEEE.

关键词： Face recognition

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Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning 2

Comparative Analysis of Various Models for Potato Leaf Disea...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Krishnakumar, B. Kousalya, K. Prakash, K.V. Indhu Ida, S. Jhansi Ravichandra, B. Rajeshkumar, G. Kongu Engineering College Dept of Cse Perundurai India R.M.K Engineering College Computer Science and Business Systems Kavaraipettai India Visakhapatnam India Bannari Amman Institute of Technology Department of Information Science and Engineering Sathyamangalam India

ISBN: (纸本)9798350346640

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

关键词： Crops

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