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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dubey, Krishna Bihari Goyal, Mukta Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

The concept of cloud computing has hastily grown in reputation, allowing businesses to shop and get access to facts from remote servers. Higher educational establishments have additionally adopted cloud services for storing and dealing with academic statistics. But this additionally poses a capability chance to the security and privacy of touchy facts. To deal with those challenges, we advise an advanced intrusion detection system (IDS) to secure the cloud's academic data. This ID uses a blockchain-based total safety framework to ensure the safety and integrity of stored statistics. The proposed IDS utilizes community visitors' monitoring and analysis to stumble upon anomalies and potential protection breaches. It also employs machine-learning techniques like anomaly detection algorithms to identify suspicious activities deviating from everyday behaviour styles. In the event of a detected intrusion, the device triggers an alert to the responsible parties and makes essential movements to mitigate the assault. The combination of blockchain technology offers an additional layer of security by developing an immutable and decentralized ledger to report all data transactions. This guarantees the integrity and authenticity of the academic statistics stored on the cloud. In case of any modifications or changes to the records, the blockchain will record them, creating a transparent audit path for accountability. Furthermore, the proposed IDS also consists of a belief management gadget that evaluates cloud carrier providers' popularity and protection posture. © 2024 IEEE.

关键词： Authenticity Cloud Community Decentralized Detected

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A Steel Surface Defect Detection Model Based on YOLOv7-tiny

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第12期51卷 2074-2082页

作者： Wang, Yayun Tao, Ye Cui, Wenhua Shen, Lijia Anshan Normal University Anshan China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China

This article designs the PELAN structure based on the lightweight YOLOv7-tiny model for surface defect detection of hot-rolled steel strips. At the same time, the CA (Channel Attention) is embedded in the feature pyramid structure and the Slim-Neck structure is introduced at the neck. Through experimental result analysis, it is proved that the lightweight algorithm not only reduces model complexity, but also has better detection accuracy and processing speed. This article focuses on exploring the potential of lightweight YOLOv7-tiny models in steel surface defect detection. With the advancement of Industry 4.0, quickly and accurately detecting surface defects on steel strips has become crucial. However, traditional defect detection methods often face issues such as large computational requirements and poor real-time performance. Therefore, we have designed a novel PELAN structure that combines CA(Channel Attention)and Slim-Neck structure. The aim is to reduce model complexity while maintaining or even improving detection accuracy and processing speed. © (2024), (International Association of Engineers). All rights reserved.

关键词： Hot rolling

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Exploring the Performance of Diffusion Models in Weighted Social Networks 2

Exploring the Performance of Diffusion Models in Weighted So...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singhal, Megh Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

In the present time, social networks have become a quintessential part of people's lives, by transforming how people connect and communicate. Social networks have become an important medium for information diffusion and social interactions. The rate of diffusion often depends upon network topology, influencing power of the initial adopters and the underlying node activation criteria. Most real-world social networks are weighted in nature, with edge weights representing the tie strength between the two connected nodes. In our research, we explore and analyze the performance of popular diffusion models in weighted social networks. The performance of 4 prevalent diffusion models, namely - Independent Cascade, Weighted Cascade, Trivalency and Linear Threshold, is compared with regards to the attained spread of information. The study was performed on 2 real-world weighted social networks. © 2024 IEEE.

关键词： Independent Cascade Influence Maximization information Diffusion Linear Threshold Weighted Social Networks

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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rafPS:A shapley-based visual analytics approach to interpret traffic

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Computational Visual Media 2024年第6期10卷 1101-1119页

作者： Zezheng Feng Yifan Jiang Hongjun Wang Zipei Fan Yuxin Ma Shuang-Hua Yang Huamin Qu Xuan Song Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 18055China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science University of ReadingBerkshire RG66AHUK Center for Spatial Information Science the University of TokyoTokyo 113-00331Japan Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Recent achievements in deep learning(DL)have demonstrated its potential in predicting traffic *** predictions are beneficial for understanding the situation and making traffic control ***,most state-of-the-art DL models are consi-dered“black boxes”with little to no transparency of the underlying mechanisms for end *** previous studies attempted to“open the black box”and increase the interpretability of generated ***,handling complex models on large-scale spatiotemporal data and discovering salient spatial and temporal patterns that significantly influence traffic flow remain *** overcome these challenges,we present TrafPS,a visual analytics approach for interpreting traffic prediction outcomes to support decision-making in traffic management and urban *** measurements region SHAP and trajectory SHAP are proposed to quantify the impact of flow patterns on urban traffic at different *** on the task requirements from domain experts,we employed an interactive visual interface for the multi-aspect exploration and analysis of significant flow *** real-world case studies demonstrate the effectiveness of TrafPS in identifying key routes and providing decision-making support for urban planning.

关键词： data visualization model interpretation urban planning urban visual analytics

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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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Dehazing using Generative Adversarial Network - A Review

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SN computer science 2025年第1期6卷 1-17页

作者： Khatun, Amina Mostafiz, Rafid Shorif, Sumaita Binte Uddin, Mohammad Shorif Hadi, Md. Abdul Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Institute of Information Technology Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science: Information Technology University of Nebraska Omaha Omaha United States

Dehazing is a difficult process in computer vision that seeks to improve the clarity and excellence of pictures taken under cloudy, foggy, and rainy circumstances. The Generative Adversarial Network (GAN) has been a viable method for removing haze from photos in recent years. This is because GAN can understand intricate data patterns and provide high-quality outcomes. This paper provides a thorough examination of the most advanced strategies in dehazing utilizing GAN. The study examines the various elements utilized in GAN-based dehazing, including generator and discriminator architectures, loss functions, and training strategies. It also explores the evaluation metrics employed to assess the effectiveness of GAN-based dehazing methods. It also examines the datasets often used to train and evaluate these models. This research concludes by examining prospective avenues for future study in the domain of dehazing, employing GAN to tackle the obstacles of real-time dehazing, managing intricate scenarios with various atmospheric conditions, and enhancing the resilience of GAN-based dehazing models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Atmospheric haze Dehazing Generative Adversarial Network (GAN) Performance evaluation metrics

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Preemptive Min Max Optimal Cost Based Scheduling for Improving the Load Balancing in Virtualized Cloud Environment

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SN computer science 2024年第6期5卷 1-12页

作者： Ravikumar, K. Saravanakumar, K. Viswanathan, Anand Department of Computer Science and Engineering RRASE College of Engineering Department of Computer Science and Engineering Sri Eshwar College of Engineering Department of Information Technology Ponjesly College of Engineering

Load balancing and scheduling are essential components of cloud computing that aim to optimize resource allocation and utilization. In a cloud environment, multiple virtual machines and applications compete for shared resources, and efficient load balancing and scheduling mechanisms are crucial for ensuring optimal performance and resource utilization. The prior methodologies attains higher utilization to allocate task due to makspan problem leads more deadlock occurrence and request failures with higher energy consideration. Furthermore, the paper examines the challenges and considerations in load balancing and scheduling, such as dynamic workload variations, heterogeneity of resources, and QoS requirements. To handle this issues, to propose an Efficient Load Balancing Based Predictive Priority-based Preemptive Min max priority Load balancer (PMin-Max LB) to improve the service optimality in Decentralized cloud server. Initially the user request accessibility and task progress is evaluated through Service level Workload Impact score (SLWIS). Then introducing Workload Scaling Job vector estimation is carried by Particle Swarm Optimization (PSO). By evaluating the Makespan Instance Scaling Changeover Virtualization (MISCV) task weights are assigned to virtual machine by applying Preemptive Min max priority Load balancer (PMin-Max LB) to reduce the workload to improve the performance. The proposed system improves the impact of load balancing and scheduling on cloud performance, scalability, and cost-effectiveness. Finally proposed system attains the potential advancements in load balancing and scheduling techniques improves the computing approach in virtualization process to reduce the workload burned to optimize the heterogeneity and Quality of service. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing Load balancer Particle swarm optimization Priority scheduling Task allocation Virtual machine

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Joint Biomedical Entity and Relation Extraction Based on Multi-Granularity Convolutional Tokens Pairs of Labeling

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computers, Materials & Continua 2024年第9期80卷 4325-4340页

作者： Zhaojie Sun Linlin Xing Longbo Zhang Hongzhen Cai Maozu Guo Department of Computer Science and Technology Shandong University of TechnologyZibo255000China Department of Agricultural Engineering and Food Science Shandong University of TechnologyZibo255000China Department of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China

Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.

关键词： Deep learning biomedical joint extraction triple classification multi-granularity 2D convolution

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Illicit Events Evaluation on the Dark Web Using NSGA-2 Algorithms Based on Energy Consumption

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Informatica (Slovenia) 2024年第18期48卷 77-96页

作者： Rawat, Romil Rajavat, Anand Department of Computer Science Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India

The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality. The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr). The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms. The result represents the "F-measure" metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using *** proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Support vector machines

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