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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Shabariram, C.P. Shanthi, N. Ponnuswamy, P. Priya Subramaniyaswamy, V. Sathana, V. Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India School of Computing Sastra University Department of Computer Science and Engineering Thanjavur India

ISBN: (纸本)9798350384369

In recent years, the edge computing paradigm enables the movement of processing units and storage nearer to the data available locations. The mechanism completes the computation in a short span of time in minimum bandwidth. Edge ecosystem is a type of distributed computing that is sensitive to topology and geography;the Internet of Things is a prime instance of this. Rather than referring to a single technology, Edge computing refers to an architecture. This paper proposes a resource allocation methodology that will enliven the situation between users and edge servers. By creating continuous control at the edge servers to determine resource allocation, edge computing improves reaction time, provides high security with decreased risk, scalability, lowers transmission costs, and versatility (offload targets, migration bandwidth and computing resources). The Deterministic Policy Gradient, Deep learning and Quality Network concepts are combined in the proposed system. The continuous action space is achieved by a deterministic policy gradient. The experience relay includes a quality network. In the proposed system, the actor-critic network produces a single continuous action instead of resulting probability based actions. The critic-part uses Q-value from a quality network based on current status and activity. The goal of the proposed system is to develop a Deep Deterministic Policy Gradient methodology to allocate servers for mobile users with the help of the Edge computing while taking computation resources, offloading goals and migration bandwidth into consideration. The simulation result indicates that deterministic policy gradients integrated deep learning models improve the system performance compared with Game theory. © 2024 IEEE.

关键词： Mobile edge computing

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Revisiting 30 years of the Network Time Protocol 24

Revisiting 30 years of the Network Time Protocol

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Woo, Simon S. Computer Science and Engineering Department Applied Data Science Department Department of Artificial Intelligence Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798400701726

Since the inception of the Internet and WWW, providing the time among multiple nodes on the Internet has been one of the most critical challenges. David Mills is the pioneer to provide time on the Internet, inventing the Network Time Protocol (NTP), and synchronizing the clocks in computer systems. Now, the NTP is predominantly used on the Internet and WWW. In this paper, we revisit the NTP, and present the overview of the NTP. In particular, we highlight the advanced research effort, the SpaceNTP, to synchronize the clocks among assets and entities in space. The SpaceNTP designed for space environments will be the fundamental medium and enabling block to provide the future web services in space. © 2024 Copyright held by the owner/author(s).

关键词： Web services

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Leveraging Ensemble Model and Optimized Feature Selection to Boost Prediction Accuracy in Educational Data Mining

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SN computer science 2025年第5期6卷 1-20页

作者： Verma, Swati Kholiya, Kuldeep Bala, Kanchan Department of Computer Science & Engineering Bipin Tripathi Kumaon Institute of Technology Uttarakhand Dwarahat India Department of Applied Science Bipin Tripathi Kumaon Institute of Technology Uttarakhand Dwarahat India Department of Computer Science & Engineering Gaya College of Engineering DSTTE Bihar Gaya India

In contemporary research, educational data mining (EDM) has become a captivating field for data mining and machine learning experts, focusing on identifying factors influencing students' academic performance and predicting the likelihood of students dropping out. To uncover these influential factors, feature selection methods are employed, while various machine learning models are used to predict students at risk of underperforming. Filter-based feature selection methods are commonly used in educational data mining due to their efficiency and ability to rank important features affecting academic success. However, because of their independence from classifiers and relying on a fixed threshold or predefined feature count, filter-based methods can sometimes negatively affect model performance. To address this, the present study introduces an optimized chi-square-based feature selection technique that dynamically selects the optimal features for each learning algorithm, ensuring that model performance is not compromised. The effectiveness of five classifiers—k-Nearest Neighbour (k-NN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR)—has been evaluated using three configurations: no feature selection, traditional chi-square feature selection, and proposed optimized chi-square based feature selection. These evaluations were conducted on two distinct student datasets, one from secondary schools (DS1) and another from engineering institutions (DS2). The results demonstrated that the optimized chi-square method consistently improved prediction accuracy across all classifiers. Additionally, a bagging-based ensemble classifier, constructed using the best-performing individual classifier, further enhanced predictive performance. The highest accuracies achieved were 94.62% for DS1 and 96.36% for DS2, outperforming traditional feature selection and ensemble methods. This study presents a scalable, reliable, and stable approach to s

关键词： Academic performance Classification models Educational data mining Ensemble model Machine learning Optimized feature selection

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ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Vagale, Ghanashyam Bhat, Shashidhar Y Padma Priya Dharishini, P. Gk, Prajwal Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350374711

This paper presents a novel framework that utilizes Google's Gemini Pro Vision large language model (LLM) and natural language processing (NLP) techniques to analyze and compare resumes or CVs with job descriptions. The framework not only evaluates the compatibility between the resume and job description but also provides personalized feedback and suggestions for improvement. By identifying potential gaps in skills and qualifications, and recommending relevant resources, the framework assists both human resources professionals in streamlining candidate shortlisting and empowers candidates to enhance their CVs. Gemini processes a resume as a Base64-encoded string and cleans up the job description using NLP. It then generates a personalized report with insights, including a compatibility score, skills analysis, improvement suggestions, and resource recommendations. Initial results demonstrate the framework's effectiveness in accurately assessing candidate suitability and providing valuable feedback. Future work will focus on expanding the framework to analyze multiple resumes simultaneously and developing more sophisticated algorithms for enhanced accuracy and reliability. This framework has the potential to revolutionize the recruitment process by streamlining candidate selection, empowering candidates to improve their CVs, and ultimately leading to better candidate job matches and a more efficient and equitable hiring landscape. © 2024 IEEE.

关键词： Visual languages

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Smart Traffic Management System for Efficient Mobility and Emergency Response

Smart Traffic Management System for Efficient Mobility and E...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Sultana, Farhain Bhardwaj, Ritika Kumar, Rohan N. Gaur, Ruchir Shankar, Sahana.P. Bharadwaj, Aryan Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

This research addresses the critical issue of urban trafficcongestion exacerbated by the inadequacies of traditional traffic management systems. Inefficient adaptation to real-time traffic conditions by static traffic light timers leads to wasted time, and fuel, increased pollution, and compromised safety during peak hours and emergencies. To mitigate these challenges, this study proposes a novel Smart Traffic Management (STM) system that leverages cutting-edge technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI). The STM framework dynamically adjusts traffic flow in response to real-time data, optimizing efficiency and enhancing emergency response mechanisms. Through comprehensive simulations and case studies, this research demonstrates the potential of the STM system to alleviate urban congestion and improve overall traffic management in diverse urban settings. © 2024 IEEE.

关键词： Traffic congestion

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computer Vision assisted Deep Learning based Sitting Posture Suggestion System 3

Computer Vision assisted Deep Learning based Sitting Posture...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Vagale, Ghanashyam Prajwal, G.K. Kumar, Rohit Dharishini, P. Padma Priya Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user's posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user's daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm's efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals. © 2024 IEEE.

关键词： Cameras

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AI-Powered Mental Health Diagnosis: A Comprehensive Exploration of Machine and Deep Learning Techniques 2

AI-Powered Mental Health Diagnosis: A Comprehensive Explorat...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Supriya, M.S. Aniket, Antham Rakshitha, N.M. Aakanksha, J. Peter, Kenith Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350382952

Individuals suffering from mental illnesses frequently communicate their sentiments and emotional states on social media through their posts. It is a challenge to recognize individuals suffering from mental health illnesses at an early stage and to provide them with necessary treatment. In this paper, an experimental methodology based on publicly available Reddit data to develop a mental health diagnosis system has been proposed. Natural language processing (NLP) techniques are exploited to help machine understand social media posts from Reddit, and deep learning and machine learning models are trained to classify the social media posts into one of the following four mental illnesses: depression, anxiety, schizophrenia and bipolar. A comparative study of these models' is performed and the results revealed that random forest outperformed other models, by obtaining 90% accuracy. In this study, we also design a web-based platform for deploying the model to aid in diagnosis of mental diseases because people suffering from anxiety or depression refrain from sharing their feelings with doctors, relatives, family, or friends. © 2024 IEEE.

关键词： Diseases

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Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

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computers, Materials & Continua 2023年第4期75卷 2227-2245页

作者： Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais Mahmood Imran Ashraf Tehmina Karamat College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Engineering HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.

关键词： Diseases deep learning endoscopy gastrointestinal tract transfer learning

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Explain the Explainer: Interpreting Model-Agnostic Counterfactual Explanations of a Deep Reinforcement Learning Agent

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1443-1457页

作者： Chen, Ziheng Silvestri, Fabrizio Tolomei, Gabriele Wang, Jia Zhu, He Ahn, Hongshik Stony Brook University Department of Applied Mathematics and Statistics Stony BrookNY11794 United States Sapienza University of Rome Department of Computer Engineering The Department of Computer Science Rome00185 Italy Xi'An Jiaotong-Liverpool University Department of Intelligent Science Suzhou215000 China Rutgers University-New Brunswick Department of Computer Science PiscatawayNJ08854 United States

Counterfactual examples (CFs) are one of the most popular methods for attaching post hoc explanations to machine learning models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood;thus, they are hard to generalize for complex models and inefficient for large datasets. This article aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations. Specifically, we formulate the problem of crafting CFs as a sequential decision-making task. We then find the optimal CFs via deep reinforcement learning (DRL) with discrete-continuous hybrid action space. In addition, we develop a distillation algorithm to extract decision rules from the DRL agent's policy in the form of a decision tree to make the process of generating CFs itself interpretable. Extensive experiments conducted on six tabular datasets have shown that ReLAX outperforms existing CF generation baselines, as it produces sparser counterfactuals, is more scalable to complex target models to explain, and generalizes to both the classification and regression tasks. Finally, we show the ability of our method to provide actionable recommendations and distill interpretable policy explanations in two practical real-world use cases. © 2020 IEEE.

关键词： Reinforcement learning

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INTEROPERABILITY PLATFORMS: A COMPONENT BASED APPROACH TO DERIVE FUNCTIONAL CLASSIFICATION 9

INTEROPERABILITY PLATFORMS: A COMPONENT BASED APPROACH TO DE...

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9th International Conference on Big Data Analytics, Data Mining and Computational Intelligence, BigDaCI 2024, the 10th International Conference on Connected Smart Cities, CSC 2024 and the 16th International Conference on e-Health, EH 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Staemmler, Martin Department of Electrical Engineering and Computer Science University of Applied Sciences Stralsund Germany

ISBN: (纸本)9789898704597

Interoperability platforms (IOPs) have been and are continuously designed, deployed and used for a variety of scopes, from simple data integration, reducing heterogeneity between data sources, data management systems without and with archival function up to allowing for process support and supporting data analysis. Furthermore, the scope depends on the intended use in acute care or research or both of them as well as on the stakeholders involved from health care professionals, external cooperating organizations and up to the involvement of patients. The objective of the paper is to derive a functional classification reflecting not only the IOP development over the last years but also anticipating upcoming designs. As such, four functional levels which build on top of each other have been identified: (i) management of unstructured data, (ii) management of structured data, (iii) process support, and (iv) data analysis. For each level, the respective system architecture is sketched, including functional components and references to relevant standards, profiles, and means for semantic annotation. Furthermore, the functional classification is applied to a set of projects and initiatives and allows assigning them to functional levels, thus enabling functional discrimination. © 2024 Big Data Analytics, Data Mining and Computational Intelligence. All Rights Reserved.

关键词： Interoperability

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