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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Madhavarapu Chandan S G Santhi T Srinivasa Rao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalainagar Tamil Nadu India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalainagar Tamil Nadu India Department of Computer Science and Engineering Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India

WSN security is a current area of research for many scientists. One of the best security tools for defending the network from malicious assaults or the unauthenticated access is IDS. In this article, we suggest an optimized hybrid classification model-based IDS with certain steps to follow. In preprocessing, the issue of class imbalance data is solved using SMOTE. A collection of features, including raw features, statistical features, and entropy features are extracted in the feature extraction step. Optimal feature selection is done based on the extracted feature set via proposed SI-BWO (Self Improved Beluga Whale Optimization) algorithm. Attack detection is the next stage, in which hybrid classification will be takes place by combining the CNN and DBN model. The same SI-BWO algorithm is used to perform the hybrid model's optimum training. Once an attack has been detected, the attack mitigation procedure is then carried out.

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Building term hierarchies using graph-based clustering 6

Building term hierarchies using graph-based clustering

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6th International Conference on Natural Language Processing and Information Retrieval, NLPIR 2022

作者： Hloch, Mark Van Meegen, Markus Kubek, Mario Unger, Herwig Faculty of Electrical Engineering and Computer Science Hochschule Niederrhein - University of Applied Sciences Krefeld Germany Department of Computer Science Georgia State University Atlanta United States Fern Universität Hagen Chair of Computer Engineering Hagen Germany

ISBN: (纸本)9781450397629

Classical tasks of a librarian, such as screening and categorizing new documents based on their content, are increasingly replaced by search engines or through the use of cataloging software. A first overview of a corpus topical orientation can be achieved by combining graph-based search engines and clustering methods. Existing classical clustering methods, however, often require an a priori specification of the desired number of clusters to be output and do not consider term relationships in graphs, which is deficient from a practical point of view. Therefore, fully unsupervised graph-based clustering approaches at the term level offer new possibilities that mitigate these shortcomings. Within this work, a set of novel graph-based clustering algorithms have been developed. The hierarchical clustering algorithm (HCA) forms term hierarchies by iteratively isolating nodes of a given co-occurrence graph based on the evaluation of the edge weight between the nodes. Based on the co-occurrence graph inherent relationships of terms, a new graph is built agglomerative forming individual term clusters of related terms. The feasibility of the outlined methods for text analysis is shown. © 2022 ACM.

关键词： Cluster analysis

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An Efficient CNN-Based Hybrid Classification and Segmentation Approach for COVID-19 Detection

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computers, Materials & Continua 2022年第3期70卷 4393-4410页

作者： Abeer D.Algarni Walid El-Shafai Ghada M.El Banby Fathi E.Abd El-Samie Naglaa F.Soliman Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh 84428Saudi Arabia Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia 44519Egypt

COVID-19 remains to proliferate precipitously in the *** has significantly influenced public health,the world economy,and the persons’***,there is a need to speed up the diagnosis and precautions to deal with COVID-19 *** this explosion of this pandemic,there is a need for automated diagnosis tools to help specialists based onmedical *** paper presents a hybrid Convolutional Neural Network(CNN)-based classification and segmentation approach for COVID-19 detection from Computed Tomography(CT)*** proposed approach is employed to classify and segment the COVID-19,pneumonia,and normal CT *** classification stage is firstly applied to detect and classify the input medical CT ***,the segmentation stage is performed to distinguish between pneumonia and COVID-19 CT *** classification stage is implemented based on a simple and efficient CNN deep learning *** model comprises four Rectified Linear Units(ReLUs),four batch normalization layers,and four convolutional(Conv)*** layer depends on filters with sizes of 64,32,16,and 8.A2×2windowand a stride of 2 are employed in the utilized four max-pooling layers.A soft-max activation function and a Fully-Connected(FC)layer are utilized in the classification stage to perform the detection *** the segmentation process,the Simplified Pulse Coupled Neural Network(SPCNN)is utilized in the proposed hybrid *** proposed segmentation approach is based on salient object detection to localize the COVID-19 or pneumonia region,*** summarize the contributions of the paper,we can say that the classification process with a CNN model can be the first stage a highly-effective automated diagnosis *** the images are accepted by the system,it is possible to perform further processing through a segmentation process to isolate the regions of interest in the *** region of interest can be assesses both automatically and through ***

关键词： COVID-19 segmentation classification CNN SPCNN CT images

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Cloud-Connected Wearable Devices for Lone Worker Protection With Wireless Sensor Networks 4

Cloud-Connected Wearable Devices for Lone Worker Protection ...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mohankumar, N. Manivannan, K.K. Babu, A. Ramesh Sampoornam, M. Maria Kovarasan, Rajesh Kambattan Srinivasan, C. Nagpur Campus Maharashtra Pune India Kcg College of Technology Department of Mechatronics Engineering Tamil Nadu Chennai India School of Computer Applications Vidya Vikas Institute of Engineering and Technology Karnataka Mysore India J.J College of Engineering and Technology Department of Information Technology Tamil Nadu Tiruchirappalli India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Various sectors continue to prioritize lone worker safety, which requires creative ways to provide quick support and monitoring. To safeguard lone workers, this paper introduces a new method that makes use of cloud-connected wearable devices that are coupled with Wireless Sensor Networks (WSNs). The system design relies on sensors embedded in wearable devices that can monitor a range of environmental elements as well as the vital signs of workers. A central cloud platform receives data in real-time via wireless communication from these devices and processes and analyzes it. The system may detect possible dangers or crises using smart techniques and immediately notify those who are supposed to react or the proper authorities. Cloud computing also makes it easy to store, analyze, and monitor large amounts of data over time, which is great for doing proactive risk assessments and improving safety standards. It demonstrates solution's capacity to improve lone worker safety, reduce reaction times, and mitigate hazards via simulations and actual deployment scenarios, efficacy and dependability. This growing body of knowledge on how to protect lone workers in various workplaces by using the power of IoT and cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Impact of Motorcycle Placement on Travel Time and Traffic Dynamics at Urban Intersections 4

Impact of Motorcycle Placement on Travel Time and Traffic Dy...

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4th International Conference on Innovative Research in Applied science, engineering and Technology, IRASET 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed Emsi Moroccan School Of Engineering Sciences Lamigep Research Laboratory Marrakech Morocco Marrakech Morocco University Of Aix-Marseille Department Of Computer Science Marseille France

ISBN: (纸本)9798350309508

This paper delves into a comprehensive exploration of the impact of motorcycle positioning on travel times at signalized intersections in urban settings. Leveraging a synergistic approach that integrates data analysis and VISSIM simulation, the study focuses on three key intersections in Marrakech, dissecting traffic dynamics during distinct time intervals. The analysis reveals consistent trends, showcasing that Scenario 2, where motorcycles are separated to the right of cars, consistently yields lower travel times compared to Scenario 1, where motorcycles navigate in front of cars. VISSIM simulations visually underscore these findings, emphasizing the efficiency gains achieved through the introduction of dedicated motorcycle lanes. The discussion articulates the significance of these results, offering insights into the potential benefits of segregated motorcycle traffic for urban traffic planning. This research contributes valuable perspectives and actionable considerations for optimizing intersection efficiency and reducing travel times in the context of urban mobility. © 2024 IEEE.

关键词： Travel time

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Integrated Fuzzy Logic and Data Mining Approach for Assessing Groundwater Vulnerability to Sea-Water Intrusion 16th

Integrated Fuzzy Logic and Data Mining Approach for Assessin...

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16th International Conference on Application of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools, ICAFS 2023

作者： Sadikoglu, Fahreddin Nourani, Vahid Najafi, Hessam Maleki, Sana Paknezhad, Nardin Jabbarian Department of Computer Engineering Near East University Nicosia Cyprus Department of IT Odlar Yurdu University Baku Azerbaijan Center of Excellence in Hydroinformatics Faculty of Civil Engineering University of Tabriz Tabriz Iran College of Environmental Science and Engineering Nankai University Tianjin China

ISBN: (纸本)9783031762826

The GALDIT model is used for the assessment of the aquifer vulnerability of Groundwater (GW), but it relies on expert judgment that contains uncertainty and is one of its weaknesses. To tackle the challenge of managing uncertainty and identifying specific vulnerabilities, this study employed a combination of Mamdani Fuzzy Logic (MFL) and data mining techniques. Then the obtained results were compared with the traditional GALDIT method. According to the Electrical Conductivity (EC) values, the Heidke Skill Score (HSS) and Total Accuracy (TA) criteria were applied to assess the outcomes of the applied methods. The surrounding aquifers of Urmia Lake was case study in this paper. The findings indicated that the GALDIT index varied from 2 to 8. The TA and HSS values in the MFL modeling were determined to be 0.59 and 0.48, respectively, therefore MFL led to more accurate and reliable results for evaluating GW vulnerability compared to the traditional GALDIT method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Salt water intrusion

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Efficient Road Status Monitoring in Vehicular Ad-Hoc Networks: An Optimization Framework 25

Efficient Road Status Monitoring in Vehicular Ad-Hoc Network...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Samara, Ghassan Turki, Hussain Mohammed Odeh, Mahmoud Al-Oraiqat, Anas Aljaidi, Mohammad Alazaidah, Raed Al-Mousa, Mohammad Rasmi Banimustafa, Ahmed Kanan, Mohammad Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates University of Business and Technology Department of Industrial Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The number of cars on the road in the world is currently increasing. With the widespread usage of vehicles, a slew of issues inevitably arises. The damage of the road can be annoying to the drivers, and congestion in traffic can waste both fuel and time. It's worth noting that one of the primary causes of car damage and aging is the state of the road surface. Driving comfort and vehicle controllability can be harmed by poor road conditions. As a result, the system of monitoring road status is crucial to reducing congestion in traffic, reducing accident rates, and protecting cars from hazardous road conditions. This paper proposes using a framework based on cloud-fog network architecture, The fog computing network architecture is a dispersed network that may process data at the fog layer to minimize cloud computing's computational load. In addition, using small and large threshold values ( ), Main Authority (MA) deals with the major conditions, and the Sub Authority (SA) deals with the minor conditions. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Text Mining for Intelligent Information Processing and Analysis 3

Text Mining for Intelligent Information Processing and Analy...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Srivastav, Adarsh Indira, G. Tiwaskar, Shweta A. Garg, Avni Singh, Shweta Das, Sunanda Vivekananda Global University Department of Computer Science & Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

textual content Mining is the process of extracting meaningful information from large volumes of unstructured text. it's far a form of synthetic intelligence used to investigate and interpret files along with consumer feedback, emails, transcripts, net logs, surveys, and on line documents. Its intention is to discover hidden shape, patterns, institutions, and correlations in textual facts in order to draw inferences and broaden new insights. textual content Mining allows a deeper level of intelligence and selection-making by means of uncovering which means from unstructured textual facts. by means of utilising present day statistical strategies and herbal Language Processing (NLP), text Mining can produce automatic fashions that interpret and translate textual content into intelligent information which can be used to make correct predictions and provide actionable insights. textual content Mining is also being used to find out new trends, categorize client sentiment, locate correlations in purchaser comments, and section document collections. eventually, text Mining is being used to assist automate selection-making by automating workflows, processing massive volumes of text in a fragment of time, and providing deep insights into patron conversations. © 2023 IEEE.

关键词： Automating Categorize Collections Conversations Insights

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Deep Learning-Driven Culvert Monitoring: A Novel Approach for Flash Flood Mitigation through Visual Blockage Analysis 9

Deep Learning-Driven Culvert Monitoring: A Novel Approach fo...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Samuel, Betty Elezebeth Alghamdi, Sultan Priya, T. Deepalakshmi, R. Kannan, Sathish Selvaraju, Mayakannan Department of Computer Science And Information Technology Jazan Saudi Arabia King Abdulaziz University Faculty of Engineering Department of Electrical And Computer Engineering Jeddah Saudi Arabia Npr College of Engineering & Technology Department of Mathematics Dindigul Tamilnadu Natham India The Tamilnadu Dr. Ambedkar Law University Department of Inter-Disciplinary Studies Tamilnadu Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamilnadu Chennai602105 India Vidyaa Vikas College of Engineering And Technology Department of Mechanical Engineering Tamilnadu Namakkal India

ISBN: (纸本)9798350394474

The primary cause of urban flash floods is often cited as trash clogging culverts. Flash floods can be avoided with the help of intelligent video analytic (IVA) methods that can extract information about blockages in order to make maintenance-related decisions in a timely manner. Knowing the percentage of culverts that are visually blocked can help prioritise maintenance at heavily blocked locations. In this paper, we introduce a deep learning-powered segmentation-classification pipeline for automatically detecting and segmenting culvert openings and then classifying them into one of four categories based on the degree to which they are visually blocked. The deep learning models underwent training using datasets sourced from the Visual Hydraulics Blockage Dataset (VHD) and Images of Culverts Blockage (ICOB). The outcomes revealed that Mask R-CNN attains the highest performance in segmentation, achieving an mAP@75 score of 77.2%. In contrast, NASNet excelled in classification tasks, with a remarkable 81.2% accuracy on the test data. To underscore the practical importance of these findings, one potential application is demonstrated, which involves using the models for monitoring visual obstructions. © 2024 IEEE.

关键词： computer vision

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