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2024 International Conference on Advances in computing research on science engineering and Technology, ACROSET 2024

作者： Sehta, Neha Shah, Naman Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Presently, educational organizations are utilizing manual procedures to assign subjects to faculties depending on their own preferences, which gives rise to prejudice and therefore, discontentment. The best approach would be to allocate the most qualified individual to instruct the appropriate course, taking into account their knowledge and experience. Many instructional units rely on manual trial-and-error methods, which are time-consuming, inefficient, and prone to biased judgment. Moreover, the distribution is likely disadvantageous for several academic personnel. This study introduces an automated allocation of subjects to the faculties, taking into account the preferences in an effective way. The subject allocation problem is mapped into a maxflow problem and solved using Edmonds-Karp algorithm. A satisfaction metric can be computed to evaluate the effectiveness of the solution proposed which is average calculated on the distribution of subject as per the choice of faculty. © 2024 IEEE.

关键词： Automation

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Unveiling Security Vulnerabilities in NFTs: A Comprehensive Risk Assessment 4

Unveiling Security Vulnerabilities in NFTs: A Comprehensive ...

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

作者： Verma, Ravi Chandrawanshi, Kamlesh Soni, Gaurav Jain, Garima Nigam, Shekhar Jain, Neelesh Vit Bhopal University School of Computing Science and Engineering Bhopal India Samrat Ashok Technological Institute Department of Computer Science Vidisha India Sam Global University School of Computer Science and Engineering Bhopal India Prestiage Institute of Science and Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331518981

The biggest invention of Non-Fungible Tokens System (NFTs), that defines its role digitally using the self-generated concept of proof of ownership, which comes as a result of continues progress and advancement in Blockchain Technology This study aims to provide a comprehensive understanding of NFTs, their foundational technology, and the platforms enabling their trade. It highlights the key challenges of implementing NFT technology, focusing on issues related to ownership, governance, security, privacy, and environmental concerns. The research explores the inherent risks in the evolving NFT ecosystem, offering insights into the nature and scale of these threats. Additionally, it proposes a set of actionable solutions for participants and stakeholders to mitigate these risks. By categorizing core hazards within a simplified taxonomy, this paper identifies key vulnerabilities and outlines strategies for addressing them to enhance the safety and sustainability of the NFT space. © 2024 IEEE.

关键词： Differential privacy

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An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

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computer Modeling in engineering & sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

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Optimized Biometric Key Management System for Enhanced Security 11th

Optimized Biometric Key Management System for Enhanced Secu...

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11th International Conference on Intelligent computing and Applications, ICRTC 2023

作者： Kaur, Prabhjot Kumar, Nitin School of Computing DIT University Uttarakhand India Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering PEC Deemed to be University Chandigarh India

ISBN: (纸本)9789819717231

Biometric cryptosystems are security tools that use an individual’s distinctive physical or behavioral characteristics to secure sensitive data. Any biometric cryptosystem’s effectiveness is dependent on how well the features are extracted. The suggested solution uses an evolutionary algorithm to first optimize the retrieved attributes. Second, fuzzy vault is used to safeguard these optimized features. The fuzzy vault system is a well-known biometric cryptography device that creates a vault by using a polynomial equation, protecting the biometric information. However, if an attacker has access to the polynomial equation, the security of the fuzzy vault scheme may be jeopardized. The shuffled frog leaping algorithm (SFLA) method has been suggested as a way to improve the security of the fuzzy vault technique in order to overcome this drawback. The SFLA is a meta-heuristic optimization method that resolves challenging optimization issues by simulating the social behavior of frogs. This meta-heuristic technique is used in this paper’s suggested strategy to produce and secure optimized points. The experiments are performed onn AMI and MMU2 databases. The results achieve higher levels of performance compared to the traditional schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optimization

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Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

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computers, Materials & Continua 2023年第6期75卷 6251-6267页

作者： Uzma Afzal Tariq Mahmood Ali Mustafa Qamar Ayaz H.Khan Department of Computer Science Federal Urdu University of Arts Science and TechnologyKarachiPakistan Department of Computer Science Institute of Business AdministrationKarachiPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Computer Engineering Department King Fahd University of Petroleum and MineralsDhahranSaudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence Dhahran31261Saudi Arabia

A complex Laboratory Developed Test(LDT)is a clinical test developed within a single *** is typically configured from many fea-ture constraints from clinical repositories,which are part of the existing Lab-oratory Information Management System(LIMS).Although these clinical repositories are automated,support for managing patient information with test results of an LDT is also integrated within the existing ***,the support to configure LDTs design needs to be made available even in standard LIMS *** manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain *** is a risky process and can lead patients to undergo unnecessary *** proposed an optimized solution(opt-LDT)based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in ***-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the *** tested and validated opt-LDT for a local secondary care hospital in a real healthcare *** results,averaged over ten runs,show that opt-LDT resolves 90%of inconsistencies while taking between 6 and 6.5 s for each ***,positive feedback based on a subjective questionnaire from clinicians regarding the performance,acceptability,and efficiency of opt-LDT motivates us to present our results for regulatory approval.

关键词： Artificial intelligence health informatics evolutionary algorithms genetic algorithms feature selection laboratory developed test

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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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Machine Learning Based Earlier Identification of Liver Disease Using Ultrasound Images 1st

Machine Learning Based Earlier Identification of Liver Disea...

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1st International Conference on Intelligent computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

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Potato Leaf Disease Detection Method Based on the YOLO Model 4

Potato Leaf Disease Detection Method Based on the YOLO Model

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Srivastava, Animesh Rawat, Bhupender Singh Bajpai, Prashant Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University College of Smart Computing Roorkee India Sr Institute of Management & Technology Department of Computer Science & Engineering Lucknow India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350393354

Global potato production is at risk because of potato leaf diseases, which cause huge economic losses. To ensure crop productivity and disease management, their efficient and accurate localization is of utmost importance. The YOLOv7 deep learning model is presented in this paper as a means of disease detection and classification in potato leaves. The suggested approach uses YOLOv7's superior detection accuracy to successfully identify and categorize potato leaf diseases. To train the model, we used a wide variety of images showing both healthy and diseased potato leaves. Verified by the test results, the suggested methodology successfully identified and classified diseases affecting potato foliage with a 98.1% accuracy rate. Through early disease detection and prompt control measure implementation, this method holds great promise for enhancing the yield and quality of potato crops in precision agriculture systems. The proposed framework shows great promise for practical implementation, as the experimental results confirm that it offers enhanced accuracy in detecting and predicting potato crop diseases. © 2024 IEEE.

关键词： Losses

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Machine Learning-Based Prediction of COVID-19: A Robust Approach for Early Diagnosis and Treatment 5th

Machine Learning-Based Prediction of COVID-19: A Robust Appr...

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5th International Conference on Trends in Computational and Cognitive engineering, TCCE 2023

作者： Johora, Fatema Tuj Mahfuja, Israt Binte Masuqur Rahman, A.N.M. Rahman, Md Mosfikur Rahman, Md Sadekur Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Daffodil Bangladesh Department of Information Systems Kobe Institute of Computing Kobe Japan

ISBN: (纸本)9789819719228

The worldwide health catastrophe sparked by the COVID-19 epidemic continues, emphasizing the need for novel solutions in prediction, early diagnosis, and treatment. While vaccine development has progressed, the virus’s eradication remains questionable. This research presents a strong machine learning-driven approach to addressing this complicated problem. Rapid viral transmission and the lack of a single cure hamper illness diagnosis and treatment, particularly in highly populated developing countries like Bangladesh. Against this context, machine learning and artificial intelligence have emerged as critical instruments for the improvement of health care and medical research. This research investigates several machine learning algorithms and approaches that show potential for COVID-19 prediction. Among these is an algorithm that assesses individual vulnerability and environmental variables, allowing for self-diagnosis and early intervention, reducing the strain on healthcare institutions and government resources. This study demonstrates how machine learning may improve forecast accuracy and provide proactive treatment in pandemic management. Advanced techniques, such as K-nearest neighbor, Decision Tree, Random Forest, AdaBoost, XGBoost, Stochastic Gradient Descent, Linear SVC, Perceptron, Naive Bayes, Support Vector Machines, Logistic Regression, and Discriminant Analysis, are essential for achieving the best results. Finally, this research emphasizes the need for novel responses to global health challenges. Healthcare systems may improve their forecasting capacities and early intervention techniques by leveraging machine learning. This paper highlights technology’s revolutionary impact in redefining healthcare paradigms and encouraging resilience in the face of enormous difficulties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

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computer Modeling in engineering & sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree Lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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