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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Harnish Joshi Nihar Bharatia Urmil Sakariya Dweepna Garg Parth Goel Department of Computer Engineering Faculty of Technology & Engineering (FTE) Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of Science & Technology (CHARUSAT) Changa India Department of Computer Science and Engineering Faculty of Technology & Engineering (FTE) Devang Patel Institute of Advance Technology & Research (DEPSTAR) Charotar University of Science & Technology (CHARUSAT) Changa India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Face recognition systems are essential in practically every industry in our digital age. One biometric that is frequently utilized is face recognition. It is helpful for security and has a ton of other advantages, identity, and authentication. Despite being less accurate than fingerprint and iris recognition, it is nonetheless commonly utilized because it is a non-invasive and contact-free technique. Additionally, facial recognition systems can be used to track attendance in workplaces, colleges, and schools, among other places. By solving problems with manual attendance procedures, like time consumption and the possibility of proxy attendance, this system seeks to develop an effective facial recognition-based classroom attendance system. As a result, this mechanism becomes more necessary. There are four components to this system capture face, train model, detect and recognize faces, update attendance record, and face detection. The photos of the children in the classroom are what make the data folder. The Haar-Cascade classifier and the Local Binary Pattern Histogram technique are used, respectively, for face detection and recognition. The suggested technique achieves approximately 80% accuracy when three to four students are present. Faces from live streaming video are identified and discovered. As a quick and safe substitute for manual attendance procedures, attendance is tracked in XLS sheets and forwarded to the appropriate faculty members at the conclusion of the session.

关键词： Industries Accuracy Face recognition Manuals Machine learning Streaming media Information age Face detection Security Iris recognition

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Assessment of the Feasibility of Employing Chaos Synchronization in Two Cascaded Microresonators for Secure Data Transmission

Assessment of the Feasibility of Employing Chaos Synchroniza...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Lemcke, Deniz Moreno, David Fujii, Shun Nakashima, Ayata Uchida, Atsushi Tanabe, Takasumi Department of Electronics and Electrical Engineering Faculty of Science and Technology Keio University Yokohama Japan RWTH Aachen University Aachen Germany Polytechnic University of Valencia Valencia Spain Department of Physics Faculty of Science and Technology Keio University Yokohama Japan Department of Information and Computer Sciences Saitama University Saitama Japan

ISBN: (纸本)9798350345995

As the limits of computational power continue to increase, concern has arisen regarding insufficient security along with a demand for improvements in encryption methods for data transmission, which might be achieved by chaos synchronization [1, 2]. Using modified Lugiato-Levefer equations (LLEs), we numerically explore two cascaded microresonator frequency combs [3]. To obtain a chaotic state, both cavities are pumped by continuous-wave lasers until modulation instability (MI) combs are generated. By injecting the output of the first resonator into the second resonator, both chaotic fields in the cavities are synchronized. In this work, we investigate to what extent the chaos is synchronized and how this phenomenon may be implemented in existing data transmission systems over a chaotic channel to enable secure communication. © 2023 IEEE.

关键词： Pumping (laser)

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Block Chain Technology Implementations for Secured Financial Transactions using Cloud Environment 1

Block Chain Technology Implementations for Secured Financial...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Lakshmi, T Vara Raja, Naveen S.M Ramadass, Sudhir Narayanan, Sreekumar Sreenivas, Velagapudi Subramanian, Balambigai Institute of Aeronautical Engineering Department of MBA Hyderabad India Department of Computer Science and Engineering Chilukuru village R R Dist Telangana Moinabad India Sterck Systems Chennai India Botho University Faculty of Engineering and Technology Network Infrastructure and Management Department Gaborone Botswana Dhanekula Institute of Engineering and Technology Department of Computer Science and Engineering Vijajayawada Krishna dt A.P Ganguru India Kongu Engineering College Department of ECE Perundurai638060 India

ISBN: (纸本)9781665456876

The advancement of technology has an influence on consumers' decision to switch from executing your financial information in a conventional way to doing so online. Contracts are encouraged by the use of cloud computers or its key feature of accessibility from everywhere. The enormous amount of data handled by the cloud platform used to facilitate money transfers necessitates maintaining a high degree of security for all those important passwords stored there. Although the conventional authentication methods continue to work well in protecting data in cloud, more sophisticated cyber attacks against cloud-based storage of information still exist. Additionally, using typical cryptographic primitives to secure cloud data requires a significant amount of operation time due to the need to execute verification checks for both valid and illegitimate access control. © 2022 IEEE.

关键词： Authentication

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Depression Intensity Classification from Tweets Using Fast Text Based Weighted Soft Voting Ensemble

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computers, Materials & Continua 2024年第2期78卷 2047-2066页

作者： Muhammad Rizwan Muhammad Faheem Mushtaq Maryam Rafiq Arif Mehmood Isabel de la Torre Diez Monica Gracia Villar Helena Garay Imran Ashraf Department of Artificial Intelligence The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Information Technology Khwaja Fareed University of Engineering and ITRYKhan64200Pakistan Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalnagar63100Pakistan Department of Signal Theory and Communications and Telematic Engineering Unviersity of ValladolidPaseo de Belén 15Valladolid47011Spain Faculty of Social Science and Humanities Universidad Europea del Atlántico Isabel Torres 21Santander39011Spain Department of Project Management Universidad Internacional IberoamericanaCampeche24560México Fundación Universitaria Internacional de Colombia Bogotá111611Colombia Universidad Internacional Iberoamericana AreciboPuerto Rico00613USA Universidade Internacional do Cuanza Cuito46703Angola Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541South Korea

Predicting depression intensity from microblogs and social media posts has numerous benefits and applications,including predicting early psychological disorders and stress in individuals or the general public.A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media *** study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces(APIs).A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the ***,an algorithm is developed to annotate the data into three depression classes:‘Mild,’‘Moderate,’and‘Severe,’based on International Classification of Diseases-10(ICD-10)depression diagnostic *** baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the *** FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model,which significantly increases the depression classification performance to an 84%F1 score and 90%accuracy compared to ***,a FastText-based weighted soft voting ensemble(WSVE)is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual *** proposed WSVE outperformed all baselines as well as FastText alone,with an F1 of 89%,5%higher than FastText alone,and an accuracy of 93%,3%higher than FastText *** proposed model better captures the contextual features of the relatively small sample class and aids in the detection of

关键词： Depression classification deep learning FastText machine learning

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Cloud-Powered Neural Networks for Sustainable Fishing Practices and Stock Management System 4

Cloud-Powered Neural Networks for Sustainable Fishing Practi...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Suresh, M. Xavier Prathik, A. Arun, V. Venugopal, R. Ganeshbabu, T.R. Muthulekshmi, M. School of Advanced Sciences and Languages Vit Bhopal University Department of Physics Madhya Pradesh Bhopal India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Tamil Nadu Chennai India Department of Mathematics Tamil Nadu Salem India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Sustainable fishing methods and efficient stock management are crucial considering growing environmental concerns;fishing practices and effective stock management are imperative. This research presents a novel approach that uses neural networks (NN) hosted in the cloud to transform sustainable fishing and stock management. This method trains NN to make predictions by combining several data sources, such as satellite images, weather patterns, and catch records. These models can reliably predict fish stocks' dynamics, giving stakeholders the information needed to make informed decisions. Crucial for real-time processing and storage of massive information is the scalability and flexibility offered by cloud infrastructure. This framework provides the appropriate usage of resources while simultaneously optimizing fishing tactics, which helps protect marine ecosystems. It demonstrates that method promotes sustainable fishing via thorough case studies and simulations. It provides a flexible and scalable solution to the problems confronting marine biodiversity and the fishing industry by combining advanced technology with ecological principles. To ensure a more egalitarian and sustainable future for fisheries management on a global scale, method is a giant leap forward in balancing commercial interests with environmental protection. Integrating data, ensuring sensors are accurate, managing huge datasets, managing regulatory compliance, and encouraging stakeholders to work together for sustainable practices are all challenges. © 2024 IEEE.

关键词： Fisheries

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Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leveraging AI and 6G for Enhanced Productivity 17

Low-Cost Green Computing-as-a-Service Testbed for SMEs: Leve...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Patwary, Mohammad N. Khan, Samiya Nawaz, Syed Junaid University of Wolverhampton Faculty of Science and Engineering WolverhamptonWV1 1LY United Kingdom University of Greenwich School of Computing and Mathematical Sciences LondonSE10 9LS United Kingdom Comsats University Islamabad Department of Electrical & Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350351637

The exponential growth in computing devices and the emergence of high-computing-dependent applications have led to a significant increase in energy demand. In response, this paper proposes the development of a novel green computing testbed aimed at addressing energy efficiency and sustainability in computing infrastructure. The testbed integrates highly energy-efficient computing infrastructure, a virtual machine-driven on-demand computing ecosystem, and Green AI model development as a service. The state-of-the-art 5G/6G infrastructure and services, such as ultra-low-latency communications, are investigated and identified as suitable for connecting the zero clients (with minimal capabilities) to remote computing and other facilities. Through a comprehensive measurement campaign, the testbed's energy efficiency and sustainability have been evaluated. This research presents a pioneering effort towards achieving sustainability in computing to support Small and Medium-sized Enterprises (SMEs) to enhance productivity with AI, with reduced cost, energy, and minimizing e-waste. © 2024 IEEE.

关键词： Green development

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Smart Glove with Mobile Application to Detect Static Arabic Hijaiyah Hand Code for Quran Recitation

Smart Glove with Mobile Application to Detect Static Arabic ...

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Learning and Technology Conference (L&T)

作者： Mohamed Elgilani Eltahir Elshareif Nurul Aswani Ahmad Alias Nazean Jomhari Hazrina Sofian Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

The use of assistive technology in the field of education is now a common practice in today’s tech-driven era. The implementation is quite rampant in all levels and sections of education, including by special needs students with hearing impairments. With the help of technology or Internet of Things (IoT), they are offered with better opportunities to improve their communication and the teaching and learning process especially when it comes to sign language. However, the situation is quite different for Muslim deaf individuals. Sign language is not only used for communication but also as a form for reciting the Quran. Yet, the utilization of technology in the area of sign language for Quranic recitation is still limited. Hence, the goal of this study was to design and develop a sensor-based sign language recognition device consisting of a sensory smart glove and mobile application to detect Arabic sign language alphabets used in Quran recitation. The experimental result shows that the gesture recognition rate of 28 different Arabic alphabet signs, performed by 7 different signers, is 79.08%. The significance of the system lies in its mobility and capability to recognize the hand gestures regardless of the background noise.

关键词：

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Reinforcement-Learning-Based Cooperative Dynamic Weapon-Target Assignment in a Multiagent Engagement

Reinforcement-Learning-Based Cooperative Dynamic Weapon-Targ...

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AIAA science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Merkulov, Gleb Iceland, Eran Michaeli, Shay Gal, Oren Barel, Ariel Shima, Tal Faculty of Aerospace Engineering Technion - Israel Institute of Technology Haifa3200003 Israel Hebrew University of Jerusalem Jerusalem9190501 Israel Israel Institute of Technology Haifa3200003 Israel Hatter Department of Marine Technologies University of Haifa Haifa3498838 Israel Faculty of Computer Science Technion - Israel Institute of Technology Haifa3200003 Israel Faculty of Aerospace Engineering Technion - Israel Institute of Technology AIAA Haifa3200003 Israel

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper considers a multiagent Shoot-Shoot-Look engagement scenario, in which multiple pursuers act against multiple evaders with predefined motion. The pursuers are arranged in two successive waves - the first wave engages the a-priori allocated evaders directly, and the second wave trails behind to assist with the pursuit of the evaders that survive the firstwave. The scenario objective is to maximize the number of intercepted evaders under the given time constraints. To facilitate the intercept performance, the second-wave pursuers are guided to intermediate virtual targets that allow several reallocation options when the actual first-wave engagement outcomes are known. The choice of the virtual targets influences the time and maneuver required from the pursuer during the engagement, which are related to the intercept probabilities. We formulate the problem of second-wave allocation as a stochastic Markov Decision Process. Due to the special problem structure, a reward expression based on the predictions of intercept probabilities is developed. Using this reward function, a Reinforcement-Learning-based strategy is proposed for the virtual target allocation for the second-wave pursuers. An alternative Greedy algorithm is designed based on maximizing individual contribution increments into intercept probabilities. Sequential decentralized decision-making architecture is used to implement both approaches. The simulation demonstrate that the Reinforcement-Learning-based solution wins slightly over Greedy, and the proposed greedy heuristic approximates well the Reinforcement-Learning solution. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Stochastic systems

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Robust Learning under Hybrid Noise

arXiv

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arXiv 2024年

作者： Wei, Yang Chen, Shuo Ye, Shanshan Han, Bo Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China RIKEN National Science Institute Japan Australian Artificial Intelligence Institute Faculty of Engineering and IT Sydney Australia Department of Computer Science Faculty of Science Hong Kong Baptist University Hong Kong

Feature noise and label noise are ubiquitous in practical scenarios, which pose great challenges for training a robust machine learning model. Most previous approaches usually deal with only a single problem of either feature noise or label noise. However, in real-world applications, hybrid noise, which contains both feature noise and label noise, is very common due to the unreliable data collection and annotation processes. Although some results have been achieved by a few representation learning based attempts, this issue is still far from being addressed with promising performance and guaranteed theoretical analyses. To address the challenge, we propose a novel unified learning framework called "Feature and Label Recovery" (FLR) to combat the hybrid noise from the perspective of data recovery, where we concurrently reconstruct both the feature matrix and the label matrix of input data. Specifically, the clean feature matrix is discovered by the low-rank approximation, and the ground-truth label matrix is embedded based on the recovered features with a nuclear norm regularization. Meanwhile, the feature noise and label noise are characterized by their respective adaptive matrix norms to satisfy the corresponding maximum likelihood. As this framework leads to a non-convex optimization problem, we develop the non-convex Alternating Direction Method of Multipliers (ADMM) with the convergence guarantee to solve our learning objective. We also provide the theoretical analysis to show that the generalization error of FLR can be upper-bounded in the presence of hybrid noise. Experimental results on several typical benchmark datasets clearly demonstrate the superiority of our proposed method over the state-of-the-art robust learning approaches for various noises. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Study and prediction of prostate cancer using fuzzy inference system

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Materials Today: Proceedings 2022年 56卷 157-164页

作者： Boadh, Rahul Aarya, Deo Datta Dahiya, Mamta Rathee, Rakesh Rathee, Sarita Kumar, Ajay Jain, Sarika Rajoria, Yogendra Kumar Department of the Mathematics School of Basic and Applied Science K. R. Mangalam University Sohna Road Haryana Gurugram122103 India Department of the Mathematics Acharya Narendra Dev College University of Delhi India Department of Computer Science & Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India University Institute of Engineering & Technology Maharshi Dayanand University Haryana 124001 Rohtak India Department of Electrical Engineering DPG Institute of Technology and Management Haryana Gurgaon122001 India Department of Mechanical Engineering Faculty of Engineering and Technology Shree Guru Govind Singh Tricentenary University Haryana Gurugram122103 India Department of Mathematics Amity School of Applied Sciences Amity University Haryana Gurugram122413 India

The essential driver of passing among men on the planet is prostate disease. In the chief stage, for early identification of the prostate disease by applying Fuzzy Inference System (FIS) has been depicted in this study. The age of the patient, Prostate Volume (PV), % Free PSA (FPSA) and Prostate Specific Antigen (PSA) are utilized as input parameters in FIS for computing the risk factor of the prostate cancer. In this study Mamdani method is used for evaluation and analysis of the Prostate Cancer Risk (PCR). The present FIS additionally required the base estimation decreases horribleness lessens dismalness, mortality and more dependable from other system. It can likewise be considered in medicine to decrease manual assignments and human mistake. This review advocate that, this FIS might consequence on work on the productivity to recognized the distinctive kind of the malignant growths just as lessening the expense of care of the patients. © 2022

关键词： Risk assessment

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