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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Manoj, P. Varun, C.M. Keerthi Kumar, N. Padhye, Pradip Kumari, K. Aruna Das, Puja Sarkar, Swagata Vidyavardhaka College of Engineering Department of Civil Engineering Mysore India R.M.K. Engineering College Department of Computer Science and Business System Kavaraipettai India Bms Institute of Technology and Management Department of Mechanical Engineering Bengaluru India School of Management Dr. Vishwanath Karad Mit World Peace University Pune India Gitam University Department of Mathematics Visakhapatnam India Hiralal Mazumder Memorial College for Women Department of Computer Science Kolkata India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350336238

Due to the lack of a mechanism in place to check online for parking availability, it is now quite difficult to find a parking spot in congested regions. Imagine being able to access information about parking slot availability on your phone and not having to move around to do so. The cutting-edge parking system powered by IoT can overcome this issue. The IoT-based parking system makes it simple to check the availability of parking spaces online. With the aid of this device, the parking system might be fully automated. You might have automatic admission, payment, and exit processes. A NodeMCU-based IOT-based car parking system is being created using five IR sensors, two servo motors, and NodeMCU. Three IR sensors are utilised to determine whether parking spaces are available, and two IR sensors are used at the entry and exit gates to identify vehicles. Based on the sensor value, servo motors control the gates' opening and closing. In this part, we'll show you how to upload data to the cloud that is reachable from anywhere using the Adafruit IO platform. © 2023 IEEE.

关键词： Infrared detectors

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Ransomware Detection in the Internet of Things (IoT): Challenges and Emerging Solutions

Ransomware Detection in the Internet of Things (IoT): Challe...

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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Sarah Tawfiq Albassam Khadijah Ahmed Alamoudi Shahad Saad Alshalawi Aseel Khaled Alghamdi Hessah Abdulmohsen Alnashwan Sarah Mohammed Alsaber Nazar Abbas Saqib Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (数字)9798350395839

ISBN: (纸本)9798350395846

Ransomware attacks cause significant security risks to organizations and individuals, which can lead to financial loss and reputational damage. Therefore, it is essential to mitigate such attacks. As the number of connected devices to the Internet of Things (IoT) increases, particularly in sensitive networks, the number vulnerabilities explored in the IoT landscape increases as well. These vulnerabilities present opportunities for malicious actors to exploit and launch attacks, including ransomware. Security measures in IoT against recent attacks, ransomware attacks, in particular, are one of the concerns that face IoT. Yet, developing ransomware detection and mitigation methods becomes crucial to prevent the attack from being executed, by implementing early response to avoid any losses that ransomware can cause. This paper will review existing ransomware detection methods in IoT environments, faced challenges, and solutions. Furthermore, a comprehensive comparison of detection methods based on the classification of the ransomware, limitations, and addressed challenges will be presented.

关键词： Protocols Reviews Organizations Particle measurements Virtual machining Ransomware Internet of Things

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Covert Channel Detection and Generation Techniques: A Survey

Covert Channel Detection and Generation Techniques: A Survey

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Nesmah A. AL-Khulaidi Ammar T. Zahary Muneer A.S Hazaa Adel A. Nasser Department of Computer Networks Yemen Academy for Graduate Studies Sana'a Yemen Department of Computer Science Faculty of Computing and IT University of Science and Technology Sana'a Yemen Department of Computer Science Faculty of Computer and Information System Thamar University Thamar Yemen Department of Information Systems and Computer Science Faculty of Sciences Sa'adah University Sa'adah Yemen

The use of covert communications has become more widespread recently as a solution to the information security issue. Information security can be partially solved by the discovery and creation of covert routes. Covert channel use is still in its early phases, even though information security concerns are growing as a result of quick technology advancements. As a result, several problems and concerns need to be resolved, and the future plan is foggy. This paper seeks to give a thorough comparison of earlier and more recent attempts in this field. It offers a survey of the literature on the drawbacks and gaps in conventional detection techniques, emphasizing the most recent techniques for discovering and creating these covert channels while carefully considering machine learning and deep learning-based detection techniques. Additionally, it broadens the topic to cover the crucial function that these channels play in technology. Timing channels, storage channels, and hybrid channels are just a few of the ways covert channels may be utilized to transfer data. They can be used to get around security measures like intrusion detection systems and firewalls. Additionally, they may be used to steal confidential data from a system, including passwords, credit card numbers, and intellectual property. system security is seriously threatened by covert channels, and more study on covert channel production and detection is required to stay up with the changing threat environment.

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2023

作者： Shaker Reddy, Pundru Chandra Nithyapriya, S. Sharma, Nipun Maheswari, S. Jayaram, B. Katale, Tejaswini SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Anna University Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Chennai India Presidency University Bangalore Electronics and Communication Engineering Karnataka India Sathyabama Institute of Science and Technology Department of Mathematics Tamil Nadu Chennai India Computer Science and Business System RMK Engineering College Tamil Nadu Kavaraipettai India Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350319200

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer. © 2023 IEEE.

关键词： breast histopathology-image Breast-cancer DL ML transfer-learning

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MentalQA: An Annotated Arabic Corpus for Questions and Answers of Mental Healthcare

arXiv

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arXiv 2024年

作者： Alhuzali, Hassan Alasmari, Ashwag Alsaleh, Hamad College of Computing Department of Computer Science and Artificial Intelligence Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science King Khalid University Abha Saudi Arabia College of Computer and Information Sciences Department of Information System King Saud University Riyadh Saudi Arabia

Mental health disorders significantly impact people globally, regardless of background, education, or socioeconomic status. However, access to adequate care remains a challenge, particularly for underserved communities with limited resources. Text mining tools offer immense potential to support mental healthcare by assisting professionals in diagnosing and treating patients. This study addresses the scarcity of Arabic mental health resources for developing such tools. We introduce MentalQA, a novel Arabic dataset featuring conversational-style question-and-answer (QA) interactions. To ensure data quality, we conducted a rigorous annotation process using a well-defined schema with quality control measures. Data was collected from a question-answering medical platform. The annotation schema for mental health questions and corresponding answers draws upon existing classification schemes with some modifications. Question types encompass six distinct categories: diagnosis, treatment, anatomy & physiology, epidemiology, healthy lifestyle, and provider choice. Answer strategies include information provision, direct guidance, and emotional support. Three experienced annotators collaboratively annotated the data to ensure consistency. Our findings demonstrate high inter-annotator agreement, with Fleiss' Kappa of 0.61 for question types and 0.98 for answer strategies. In-depth analysis revealed insightful patterns, including variations in question preferences across age groups and a strong correlation between question types and answer strategies. MentalQA offers a valuable foundation for developing Arabic text mining tools capable of supporting mental health professionals and individuals seeking information. © 2024, CC BY-NC-ND.

关键词： Quality control

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EAMultiRes-DSPP: an efficient attention-based multi-residual network with dilated spatial pyramid pooling for identifying plant disease

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Neural Computing and Applications 2024年第26期36卷 16141-16161页

作者： Al-Gaashani, Mehdhar S. A. M. Muthanna, Ammar Chelloug, Samia Allaoua Kumar, Neeraj School of Resources and Environment University of Electronic Science and Technology of China 4 1st Ring Rd East 2 Section Sichuan Chengdu610056 China 6 Miklukho-Maklaya St Moscow117198 Russia Department of Telecommunication Networks and Data Transmission The Bonch-Bruevich Saint-Petersburg State University of Telecommunications 6 Miklukho-Maklaya St Saint Petersburg Saint Petersburg193232 Russia Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh610101 Saudi Arabia Department of Computer Science Thapar Institute of Engineering and Technology Patiala147004 India

Convolutional neural networks (CNNs) have made substantial contributions to the domain of plant disease diagnosis, attaining noteworthy levels of accuracy. The primary objective of this study is to enhance the capabilities of CNNs within this particular field. In this article, we suggest a unique strategy utilizing MultiRes blocks and attention mechanisms to enhance the classification performance of CNNs for plant diseases. Our solution involves stacking four MultiRes blocks, each of which gradually increases the number of filters to prevent excessive propagation of memory requirements to deeper network nodes. In order to collect more spatial information, we also implement a residual link and a 1 × 1 convolutional layer. After each MultiRes block, we employ a Convolutional Block Attention Module to emphasize vital information and decrease redundant noise by inferring attention mappings along the channel and spatial dimensions. Finally, our method includes the use of a Dilated Spatial Pyramid Pooling module which is designed to extract information from the input image at various scales. Our evaluation leveraged a comprehensive dataset compiled from multiple sources, including the New Plant Diseases Dataset, Corn Dataset, and Coffee Dataset, encompassing a wide range of plant leaves both healthy and diseased, across various classes. This diverse collection of images, augmented by several techniques to enhance data variance and model robustness, provided a solid foundation for assessing the performance of our proposed model. Experiments demonstrate that this method outperforms conventional machine learning techniques and delivers high classification accuracy for plant diseases, with a precision of 99.34%, recall of 99.25%, f1-score of 99.29%, and accuracy of 99.35%. Our proposed model has the potential to considerably enhance the effectiveness and efficiency of diagnosing plant diseases in the agricultural industry. © The Author(s), under exclusive licence to Springer-

关键词： Image classification

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Automatic License Plate Detection Using Information Processing system Vision and Deep Learning 2

Automatic License Plate Detection Using Information Processi...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Sharma, Avinash Dwivedi, Ishita Dawar, Divya Saxena, Rini Barua, Tarkcshwar Goyel, Parul Chandigarh Engineering Chandigarh Group of Colleges Department of information processing system Science and Engineering Mohali140307 India Mohali140307 India Roorkee Institute of Technology Computer Science & Engineering Department Roorkee India M. M. Engineering College Maharishi Markandeshwar University CSE Department Mullana Haryana Ambala133207 India

ISBN: (纸本)9798350366570

The content of this probe is to overcome the current cognitive content with ALPR playacting, with a special accent on developing a dead and impelling playacting for distinguishing License plates from video film. The study attempts to ameliorate the presentation norms of ALPR technology by utilizing the powerful accumulation of optical character identification (OCR)[20] and deep learning based object detection algorithmic program. The core of this occupation is the use of the cutting-edge YOLOv8. object distinguishing hypothesis, which has been pains taking and disciplined on an unequaled dataset premeditated with License plate detection in mind. Focusing on quality and efficiency, the YOLOv8 model accomplish an amazing 99.8 quality rate when it comes to accurately localization and categorization License plates[18] inside video frames. This occupation aims to formalize the willingness and dependability of the advisable ALPR playacting through extensive scientific research. The project aims to intensify ALPR technology and determinant the forthcoming of transport and police investigation playactings[18] by breaking through current obstruction and establishing new presentation norms. © 2024 IEEE.

关键词： Deep learning

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Ev-IDID: Enhancing Solutions to Interactive Dynamic Influence Diagrams through Evolutionary Algorithms 21

Ev-IDID: Enhancing Solutions to Interactive Dynamic Influenc...

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21st International Conference on Autonomous Agents and Multiagent systems, AAMAS 2022

作者： Ma, Biyang Pan, Yinghui Zeng, Yifeng Ming, Zhong School of Computer Science and Engineering Minnan Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781713854333

Interactive dynamic influence diagrams (I-DIDs) are a general framework for multiagent sequential decision making under uncertainty. Due to the model complexity, a significant amount of research has been invested into solving the model through various types of either exact or approximate algorithms. However, there is no tool that allows users to specify the algorithm parameters and visualise the model solutions. In this demo, we develop an interactive I-DID system that implements most of the state-of-art I-DID algorithms and develops a new type of algorithms based on evolutionary computation. In particular, we propose a multi-population genetic algorithm for solving the I-DID models and automate the generation of behavioural models in the solutions. This demo will facilitate the I-DID research development and practical applications, and elicit a new wave of I-DID solutions based on evolutionary algorithms. © 2022 International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Decision making

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Two-timescale Optimization for E2E Network Slicing-aided Cloud-edge Collaborative networks

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IEEE Transactions on Vehicular Technology 2025年第6期74卷 9777-9789页

作者： Wang, Yunfeng Zhao, Liqiang Chu, Xiaoli Song, Shenghui Deng, Yansha Nallanathan, Arumugam Zhou, Guorong Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Xidian University Guangzhou Institute of Technology Guangzhou510100 China University of Sheffield Department of Electronic and Electrical Engineering SheffieldS10 2TN United Kingdom Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong King's College London Department of Engineering LondonWC2R 2LS United Kingdom Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

To leverage the synergy between cloud computing (CC) and edge computing (EC) to support various services while reducing the CC/EC switching overhead, the two-timescale utility maximization problem for an end-to-end network slicing-aided cloud-edge collaborative network (E2E-CECN) is formulated covering both the high throughput and low delay service requirements. To solve the utility maximization problem while dynamically adjusting the weights of the E2E-CECN utility to accommodate the variation of users' service requests, we proposed a reward comparison double deep Q network algorithm to optimize the large timescale joint virtual base station activation and CC-EC scheduling, and a reward comparison deep deterministic policy gradient algorithm to optimize the small timescale allocation of backhaul link capacity (BLC), CC/EC capability and transmission power. Numerical results show that both the high-throughput and low-delay service requirements can be satisfied simultaneously under the reasonable BLC usage and power consumption. © 1967-2012 IEEE.

关键词： Cloud platforms

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Using an Evolutionary Algorithm Such as Genetic Algorithm (Ga) to Optimize Linear Equations

Using an Evolutionary Algorithm Such as Genetic Algorithm (G...

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Neural networks and Neurotechnologies (NeuroNT), II International Conference on

作者： Abdul Sttar Ismail Wdaa Shokhan M. Al-Barzinji Hiba A. Tarish College of Education for pure Science University of Anbar Ramadi Iraq Department of Computer Networks Systems College of Computer Science and Information Technology University of Anbar Ramadi Iraq Civil Engineering Departments University of Technology Baghdad Iraq

ISBN: (数字)9798350363739

ISBN: (纸本)9798350363746

An Evolutionary algorithm is a subset of evolutionary computations. Cloning, mutation, recombination, and selection are some of the methods used by the evolutionary algorithm that were inspired by biological evolution. The problem that was addressed in this study is how to find the coefficient values of the variables in linear equations that contain a set of variables, it must equal the right side. After reviewing previous research looking for optimization, genetic algorithm, which will use in this paper, so how are going to use this strategy to solve our problems? Select the best chromosomes from this population. Perform random chromosomal hybridization. Then, within the genes of our chromosomes, produce random mutations. Will send these chromosomes for selection again. The best chromosomes among them are selected again. It crosses and turns again, and the process is repeated until a certain number of generations have passed or find the best option. After applying the genetic algorithm through the Matlab program to the linear equation, which contains six variables, for example, the results were very excellent, as the parameter values were found in a very short time and with high accuracy, completely matching the right side of the equation.

关键词： Sensitivity analysis Evolution (biology) Sociology Evolutionary computation Statistics Biological cells Optimization

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