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3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Bindu, Dubba Hima Siri, M. Shanvitha, Muvvala Sri Venkata Satya Aiswariya Milan, K. Amrita Vishwa Vidyapeetham Amrita School of Computing Bengaluru Department of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Persistent challenges in the fashion e-commerce industry include inaccurate sizing and lack of personalized experiences, which often lead to dissatisfaction and high return rates. The innovations in technology, including pose estimation and machine learning, offer promising solutions to these issues. This project offers a real-time body measurement system to increase the probability of fit accuracy and the self-confidence of the users while selecting clothes online. Utilizing MediaPipe's pose estimation framework and OpenCV, the application captures accurate measurements of body length, chest, waist, and sleeve length using an interface webcam. User-friendly web application developed using Flask allows users to choose from categories of clothes, and receive size recommendations according to their measurements. It's designed to deal with the challenges of fit accuracy and engagement. This reduces size related returns and allows for scalability and further development along the lines of more sophisticated recommendation algorithms. It integrates traditional shopping experiences with the demands of modern e-commerce. Evaluation metrics used to examine how well recommendation systems work are success rate (the proportion of matched items to the dataset), matched items (successful suggestions based on user measures), dataset size (total items processed), and matching time (evaluating computational efficiency for real-time application) are some of the metrics used in this implementation © 2025 IEEE.

关键词： Marketplaces

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PS-CoT-Adapter: adapting plan-and-solve chain-of-thought for scienceQA

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science China(Information sciences) 2025年第1期68卷 392-393页

作者： Qun LI Haixin SUN Fu XIAO Yiming WANG Xinping GAO Bir BHANU School of Computer Science Nanjing University of Posts and Telecommunications Purple Mountain Laboratories Department of Electrical and Computer Engineering University of California at Riverside

Large language models (LLMs) have recently shown remarkable performance in a variety of natural language processing (NLP) *** further explore LLMs'reasoning abilities in solving complex problems,recent research [1-3]has investigated chain-of-thought (CoT) reasoning in complex multimodal scenarios,such as science question answering (scienceQA) tasks [4],by fine-tuning multimodal models through human-annotated CoT ***,collected CoT rationales often miss the necessary rea-soning steps and specific expertise.

关键词： chain adapting plan-and-solve ps-cot-adapter scienceqa thought

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A review on network function virtualisation security challenges and design considerations

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International Journal of Networking and Virtual Organisations 2024年第4期31卷 368-393页

作者： Ahmad, Naveed Rathore, Muhammad Siraj Ngadi, Md Asri bin Faculty of Engineering University Teknologi Malaysia 81110 Malaysia Faculty of Computing Department of Computer Science Capital University of Science and Technology Islamabad46000 Pakistan

Network function virtualisation (NFV) offers several benefits to both network operators and end users. It is a more programmable and low-cost solution as compared to a traditional network. Since the network functions are implemented in software, there is a great opportunity of extending a network according to the changing requirements of the customers. However, at the same time, there are many security threats since the same commodity hardware is shared among multiple virtualised network functions. In literature, various NFV security threats are reported. In this work, our aim is to consolidate them in a more structured and organised manners. We highlight the different building blocks of a NFV framework and then explore how a particular component may become the victim of an attacker. Finally, we summarise our lesson learned and present open research issues which might be useful for NFV practitioners and researchers. © 2024 Inderscience Enterprises Ltd.

关键词： Network function virtualization

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Ransome Ware Detection Using Machine Learning and Deep Learning Models 12th

Ransome Ware Detection Using Machine Learning and Deep Learn...

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12th International Conference on Recent Trends in computing, ICRTC 2024

作者： Vivek, Vinnakota Sai Lokesh, Vadlamudi Kaushik, Tata Kumaran, U. Umah, B. Devarajan, Gopal Ganesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

This research addresses the critical challenge of ransomware detection through the use of deep learning and machine learning methods. Because ransomware is a serious threat to cybersecurity, it is imperative that advanced techniques be used for prompt detection and mitigation. Using a large dataset, the study concentrates on pertinent features necessary for efficient machine learning model training. The suggested model shows promising results in identifying and averting ransomware attacks by utilizing cutting-edge algorithms. The model’s effectiveness is confirmed by the evaluation metrics (F1 score, recall, accuracy, and precision). By providing a proactive defense against emerging ransomware threats, the findings enhance cybersecurity measures. The study not only provides a strong methodology for ransomware detection but also encourages more research in the area with the goal of continuously bolstering cyber defenses against evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhancing Academic Integrity in Online Assessments: Introducing an Effective Online Exam Proctoring Model using YOLO 2

Enhancing Academic Integrity in Online Assessments: Introduc...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Singh, Tripty Nair, Rekha R. Babu, Tina Duraisamy, Prakash Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Science and Engineering University of Wisconsin Green BayWI United States

The issue of cheating during exams has become a significant concern, gaining widespread attention from the public and media. This issue has been made worse by the transition of educational institutions to an online format, making it simpler to engage in academic dishonesty. To overcome this problem, developed an innovative solution known as the Online Exam Proctoring Model using You Only Look Once (YOLO) Model. With the use of cutting-edge computer vision algorithms, the proposed model makes use of data from cameras and microphones to automatically spot and prevent cheating during online exams. Face orientation, face spoofing detection, mouth tracking, audio analysis, mobile device recognition, and person counting are among the primary elements built into the model. Object detection algorithms, in conjunction with pre-trained YOLO weights, empower the model to detect mobile devices and count the number of individuals present in the exam environment. The automatic termination of exams upon detection of mobile devices, multiple persons, or no persons for 10 seconds acts as a strong deterrent against cheating. The suggested methodology created a reliable and effective tool for online examinations, guaranteeing the academic integrity and enabling impartial assessment of the skills and knowledge of learners. By implementing this solution, educational institutions can foster a secure online exam environment, promoting a culture of honesty and trust between students and educators. As navigate the evolving environment of education, the model stands as a reliable ally in upholding the credibility and authenticity of online learning experiences. © 2024 Elsevier B.V.. All rights reserved.

关键词： Object detection

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Comprehensive analysis of various imputation and forecasting models for predicting PM2.5 pollutant in Delhi

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Neural computing and Applications 2025年第17期37卷 11441-11458页

作者： Karnati, Hemanth Soma, Anuraag Alam, Adnan Kalaavathi, B. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India

Understanding and predicting air quality is pivotal for public health and environmental management, especially in urban areas like Delhi. This study utilizes a comprehensive dataset from the Central Pollution Control Board, detailing air pollutant concentrations at the ITO (Income Tax Office), Delhi station, from 2017 to 2023. Our focus on pollutants such as PM2.5, PM10, NO2, NH3, SO2, ozone, and CO highlights the challenges posed by missing data in environmental studies. Through the application of several imputation models, including KNN, linear regression, forward fill + backward fill, Fourier + KNN, linear interpolation, and statistical methods, we identified combination of forward fill and backward fill as the most effective method for addressing data gaps, specifically for ozone measurements. Building on this imputed dataset, we employ various forecasting models to predict PM2.5 levels, a critical pollutant. Our exploration includes time series and deep learning approaches, such as LSTM, Bi-LSTM, CNN-LSTM, GRU, Deep AR, WaveNet, TCN, ARIMA, SARIMA, and Prophet. The performance of these models is evaluated using daily data, with Bi-LSTM and LSTM with attention emerging as the top performers due to their accuracy in predicting PM2.5 concentrations. The implications of our findings extend beyond academic interest, offering practical insights for environmental policy and health advisories in Delhi. By demonstrating the efficacy of specific imputation and forecasting techniques, our research contributes to the broader effort of improving air quality monitoring and prediction. The success of Bi-LSTM and LSTM with attention models, in particular, suggests a promising avenue for future studies aiming to enhance the precision of environmental forecasts. © The Author(s) 2025.

关键词： Time series

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Real-Time Traffic Flow Optimization using Adaptive IoT and Data Analytics: A Novel DeepStreamNet Model 4

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Saranya, V.S. Subbarao, Garimidi Balakotaiah, Dega Bhavsingh, Maloth Babu, K. Suresh Dhanikonda, Srinivasa Rao School of Computing Srm Institute of Science and Technology Department of Computing Technologies Chennai603 203 India India Department of Computer Science and Engineering India Ashok: Womens Engineering College Department of Computer Science and Engineering Andhra Pradesh Kurnool India Department of Computer Science and Engineering A.P Narasaraopet India Department of Artificial Intelligence and Data Science Telangana Hyderabad India

ISBN: (纸本)9798331540364

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts © 2024 IEEE.

关键词： Convolutional neural networks

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Resilient TCP Variant Enabling Smooth Network Updates for Software-Defined Data Center Networks

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Tsinghua science and Technology 2024年第5期29卷 1615-1632页

作者： Abdul Basit Dogar Sami Ullah Yiran Zhang Hisham Alasmary Muhammad Waqas Sheng Chen Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China and also with Department of Informatics and SystemsUniversity of Management and TechnologyLahore 54660Pakistan Department of Computer Science Shaheed Benazir Bhutto UniversitySheringalUpper Dir 18050Pakistan School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha 61421Saudi Arabia Department of Computer Engineering Faculty of Information TechnologyUniversity of BahrainSakheer 32038Bahrainand also with School of EngineeringEdith Cowan UniversityPerth WA 6027Australia School of Electronics and Computer Science University of SouthamptonSouthamptonSO171BJUK

Network updates have become increasingly prevalent since the broad adoption of software-defined networks(SDNs)in data *** TCP designs,including cutting-edge TCP variants DCTCP,CUBIC,and BBR,however,are not resilient to network updates that provoke flow *** this paper,we first demonstrate that popular TCP implementations perform inadequately in the presence of frequent and inconsistent network updates,because inconsistent and frequent network updates result in out-of-order packets and packet drops induced via transitory congestion and lead to serious performance *** look into the causes and propose a network update-friendly TCP(NUFTCP),which is an extension of the DCTCP variant,as a *** are used to assess the proposed *** findings reveal that NUFTCP can more effectively manage the problems of out-of-order packets and packet drops triggered in network updates,and it outperforms DCTCP considerably.

关键词： software defined data center networks network updates DCTCP out-of-order packets packet drop SDN

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A Novel Attention-Based Method for Integrating Facial and Speech Features in Emotion Recognition 2

A Novel Attention-Based Method for Integrating Facial and Sp...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Rahul Sagar, Shraddha Kumar, Avadhesh Sharma, Vishnu Galgotias University School of Computing Science & Engineering Greater Noida India Its Engineering College Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350388916

Our research introduces a novel approach for recognizing multiple emotions by leveraging perspective-based methods. This technique integrates facial features and speech characteristics obtained from different encoders to pinpoint the most significant attributes. The distinct characteristics of speech and facial expressions, along with effective size management, prove to be highly beneficial. By concentrating on specific components, the system enhances overall accuracy. Images of facial features are captured from both low and high angles to ensure a comprehensive representation. These techniques are merged through a fusion mesh, producing several feature vectors that are subsequently utilized in the classification stage for sensitivity analysis. When tested with both data sets, this algorithm surpassed existing models, achieving a weighted accuracy (WA) of 90.3%. © 2024 IEEE.

关键词： Attention-Based Method Emotion Classification Facial and Speech Features Fusion Network Multimodal Emotion Recognition Weighted Accuracy

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