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3rd international conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2024

作者： Shakila, A. Joice Saravanan, S. Annamalai University Department of Computer and Information Science Tamilnadu Chidambaram India Annamalai University Faculty of Science Department of Computer and Information Science Tamilnadu Chidambaram India

ISBN: (纸本)9798331505714

The vital role of growing apples in global farming, both for their nutritional and economic power, shows the need to tackle diseases hitting apple orchards. Using technology, especially computer-aided diagnosis systems, offers a great way to boost the spotting and sorting of diseases. This gives big gains to the farmers by upping the quality and yield of their apples. Bringing tech progress to protect apple orchards from diseases ensures a steady flow of top-notch apples for folks worldwide. This helps the Apple business grow & flourish. Pursuing technological advancements in farming, especially through computer-aided diagnosis systems, represents a significant technological breakthrough and a critical step toward ensuring the sustainability and productivity of apple farming. The goal of this research is to implement a comprehensive approach to the detection and classification of diseases in apple leaves. This proposed methodology incorporates the Wiener filter for noise removal, which boosts image clarity and adaptive thresholding for segmenting the images to find the infected areas. The VGG-16 model helps to extract the key features from the images, and XG-Boost (Extreme Gradient Boosting) is used to closely sort the different apple leaf diseases. Together, these advanced techniques have hit an impressive accuracy rate of 95.90%, showing how good this integrated approach is at boosting disease management strategies for apple orchards. © 2024 IEEE.

关键词： Fruits

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Investigating the Effects of AI-Driven Systems on Data science Automation 15

Investigating the Effects of AI-Driven Systems on Data Scien...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Senthilkumar, K.K. Mehta, Pradnya S. Pandey, Megha Rao, Panduranga M.V. Singh, Baldev Khurana, Saniya Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Computer Engineering Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karnataka Bangalore India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

This article investigates the effect of AI-driven structures on the automation of information science. AI-pushed structures and automation of records technology have ended up an increasing number of essential because of the growing complexity of information and the numerous and ever-increasing data resources in today's digital international. This text explores the approaches in which AI-driven structures have converted how records technology is being computerized and the way it has impacted the method. Moreover, the article looks at the benefits and shortcomings of AI-driven structures in terms of automation of data science as well as the various regions which are still open for research that allows you to, in addition, improve the automation of information technology. Finally, the article proposes future study guidelines in this field, allowing the continued development of AI-driven records science automation. ©2024 IEEE.

关键词： automated information proposes shortcomings transformed

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Machine Learning as the Shield: Mitigating ARP Poisoning Attacks in Software Defined Networks 25

Machine Learning as the Shield: Mitigating ARP Poisoning Att...

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25th international Arab conference on information Technology, ACIT 2024

作者： Alquran, Rabee Al Jamal, Mahmoud Aljaidi, Mohammad Al-Jamal, Mohammad Khassawneh, Bashar Alsarhan, Ayoub Alidmat, Omar Samara, Ghassan Almatarneh, Sattam The Hashemite University Faculty of Prince Al-Hussien bin Abdullah for It Department of Information Technology Zarqa Jordan Irbid National University Department of Cybersecurity Irbid Jordan Zarqa University Department of Computer Science Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Yarmouk University Department of Statistics Irbid Jordan Amman Arab University Faculty of Computer Science and Informatics Department of Computer Science Amman Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Artificial Intelligence Zarqa Jordan

ISBN: (纸本)9798331540012

Software-defined networking (SDN) revolutionizes network management by centralizing control, but this centralization also introduces notable vulnerabilities, especially Address Resolution Protocol (ARP) poisoning attacks. These attacks exploit the ARP protocol, redirecting network traffic to malicious destinations. This research provides a deep dive into the nuances of these threats within the SDN context, outlining their mechanisms and potential countermeasures. A significant highlight of this paper is exploring Machine Learning (ML) as a pivotal mitigation approach. By analyzing and distinguishing between benign and malicious traffic patterns in real-time, ML models present a robust solution to enhance SDN security. The findings of our paper underscore the criticality of integrating these advanced predictive techniques in SDNs, paving the way for a safer and more resilient networking future. © 2024 IEEE.

关键词： ARP Poisoning Machine Learning (ML) Software-defined network (SDN)

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Compiler-provenance identification in obfuscated binaries using vision transformers

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FORENSIC science international-DIGITAL INVESTIGATION 2024年 49卷

作者： Khan, Wasif Alrabaee, Saed Al-kfairy, Mousa Tang, Jie Choo, Kim-Kwang Raymond United Arab Emirates Univ Informat Syst Secur Al Ain 15551 U Arab Emirates Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Tsinghua Univ Dept Comp Sci Beijing Peoples R China Univ Texas San Antonio Dept Informat Syst & Cyber Secur San Antonio TX USA United Arab Emirates Univ Big Data Analyt Ctr Al Ain 15551 U Arab Emirates United Arab Emirates Univ Coll IT Cybersecur Res Grp Ctr Al Ain 15551 U Arab Emirates

Extracting compiler-provenance-related information (e.g., the source of a compiler, its version, its optimization settings, and compiler-related functions) is crucial for binary-analysis tasks such as function fingerprinting, detecting code clones, and determining authorship attribution. However, the presence of obfuscation techniques has complicated the efforts to automate such extraction. In this paper, we propose an efficient and resilient approach to provenance identification in obfuscated binaries using advanced pre-trained computer-vision models. To achieve this, we transform the program binaries into images and apply a two-layer approach for compiler and optimization prediction. Extensive results from experiments performed on a large-scale dataset show that the proposed method can achieve an accuracy of over 98 % for both obfuscated and deobfuscated binaries.

关键词： Reverse engineering Compiler provenance Binary code analysis Malware analysis

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Forecasting the Impact of Deep Learning on Data science Practices: A Comprehensive Analysis 15

Forecasting the Impact of Deep Learning on Data Science Prac...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Acharjya, Kalyan Devaraj Verma, C. Umamaheswari, B. Shelke, Priya M. Kumar, Mukesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is miles information scientists are increasingly using the accuracy and efficiency of information science initiatives. Furthermore, the era has stepped forward hastily to one of these excessive levels. Many leading facts scientists throughout numerous domains have begun to contain deep mastering into their facts technological know-how workflows. In this paper, we examine and forecast the modern-day and destiny impact of deep gaining knowledge of data technology practices. We start by analyzing the distinct advantages of deep learning, including progressed accuracy, reduced guide attempt, more suitable automation, and advanced scalability. We then become aware of extraordinary applications in which deep learning is being applied and evaluate its impact on the respective information science pipelines. Sooner or later, we provide a comprehensive forecast of the capability applications of deep getting to know within the records technology subject inside the near and far destiny. Notably, we recall the capacity of deep studying to force essential modifications in how information mining, analytical choice-making, and large records packages. Through our analysis, we will forecast the ability of deep studying to revolutionize statistics technological know-how practices in extraordinary ways. © 2024 IEEE.

关键词： Automation Practices Processing Technology Unprecedented

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques 9

Sensitive Content Detection in Social Networks Using Deep Le...

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9th IEEE/ACIS international conference on Big Data, Cloud Computing, and Data science, BCD 2024

作者： Perikos, Isidoros University of Patras Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press 'Diophantus' Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350394191

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience. © 2024 IEEE.

关键词： Adversarial machine learning

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Evaluating the Performance of Object Detection Algorithm in Low Light 1

Evaluating the Performance of Object Detection Algorithm in ...

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1st IEEE international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Solanki, Arjun Vaidya, Himadri Rithak Rawat, Ruchira Sandhya Gupta, Rupesh Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Graphic Deemed to Be University Department of Computer Science and Engineering Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350372748

In this crucial area of computer vision, this study tackles the difficulties of identifying objects in dim illumination. As deep learning is advancing quickly and sophisticated CNN designs appear, this research investigates and compares four modern and advanced algorithms - Faster RCNN, Mask RCNN, SPP Net, and Mobile Net - tailored for efficient low-light object detection. Utilizing a comprehensive NOD dataset, captured under varying low-light scenarios, this research work evaluates the algorithms based on precision, recall, F1 score, and accuracy. The findings reveal the Mobile Net's exceptional performance in balancing accuracy and efficiency, which will make the system robust for real-time applications. This research not only benchmarks the algorithms quantitatively but also provides qualitative insights into their computational requirements, yielding valuable considerations for selecting suitable models in challenging illumination scenarios. © 2024 IEEE.

关键词： Object detection

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Automated Extraction of Company Names from Product Label Images: A Text Mining Approach 2

Automated Extraction of Company Names from Product Label Ima...

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2nd international conference on advanced Computing and Communication Technologies, ICACCTech 2024

作者： Kholiya, Gaurav Joshi, Vimal Pandey, Sanyam Thapa, Sashank Sharma, Vikrant Vats, Satvik Computer Science & Engineering Graphic Era Hill University Dehradun India Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798331519056

Nowadays, it is becoming more and more important to get information from various sources. One of the key details that provide us with information about the product is its name, which is prominently printed on it. In this study, we have introduced an autonomous text-mining technique to extract the company name from the images. For machine learning and natural language processing applications, this method uses the Python language and packages like scikit-learn, py-tesseract, and NLTK. The process involves employing stemming, tokenization, stop-word removal, and optical character recognition (OCR) to prepare the text that was extracted from the image. Then, using the TF-IDF vectorizer, the text is transformed into numerical vectors. Cosine similarity is used to compare the vectorized input text to a corpus of previously processed paragraphs that have the well-known description associated with different organizations. The name of the business that most closely matches the text is the outcome. The recommended method appears to have potential for accurately identifying the brand name from images of product labels, facilitating efficient information retrieval and assisting consumers in making decisions. This research can be helpful to companies in the analysis of a certain product in a given location in relation to other brands and how their product item is doing in terms of consumption in that area may also be valuable to companies. © 2024 IEEE.

关键词： Optical character recognition

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An IoT Based Electromyography Signal Transmission from sEMG Electrodes to Client's Server with ICT Infrastructure 10

An IoT Based Electromyography Signal Transmission from sEMG ...

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10th international conference on advanced Computing and Communication Systems, ICACCS 2024

作者： Pavithra, P.M. Balakrishnan, M. Anandaraj, A. Ramya, M. Sudharsan, R. Raja Sri Shanmugha College of Engineering and Technology Department of Artificial intelligence and Data Science Salem India Dr. Mahalingam College of Engineering and Technology Department of Artificial intelligence and Data Science Pollachi India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sri Shanmugha College of Engineering and Technology Department of Information Technology Salem India Sri Shanmugha College of Engineering and Technology Department of Biomedical Engineering Salem India

ISBN: (纸本)9798350384369

Bio-electric signal like Electromyography (EMG) can measure signal data from nerves and muscles separately in the particular region of affected nerve or muscles from a Human Being. For incorporating these measurements, can use a non-invasive technique. The Surface electrode can be used for measuring these problems in non-invasive fashion. Here, the idea of Internet of Things (IoT) can bring included advantage to applications with bio-electric signals to communicate to the doctors and patients such as human-PC cooperation by incorporating different advances for example, sensors, remote correspondence and information science. In this research, we worked on a remote bio-electric signals to another remote observing handling framework. A model with the contextual investigation of nerve and muscle signal acknowledgment utilizing 4-channel sEMG signals is executed. A Gaussian filter is prepared from one individual's sEMG signals with 4-nerve regions of human body such as ulnar, radial, median and radial. The introduced IoT application framework is actualized based on an 11-terminal bio-electric signal measuring device, with Arduino based Controller, Gateway, ESP8266 (Wifi) module, Server being the major mediums of remote transmission of signal data. The signal transmits at 35KB/s. In the framework, the ongoing sEMG signal from surface electrode is filtered from noise and other distortions using is given to the controller for performing the operation. © 2024 IEEE.

关键词： Gateways (computer networks)

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A Denial of Service Attack Defense Method for Roadside Units 12

A Denial of Service Attack Defense Method for Roadside Units

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12th international conference on advanced Cloud and Big Data, CBD 2024

作者： Wang, Huanbin He, Gaofeng Zhu, Haiting Xu, Bingfeng Zhang, Lu Guo, Naixuan School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Computer Science Nanjing Audit University Nanjing China School of Information Engineering Yancheng Institute of Technology Yancheng China

ISBN: (纸本)9798331511074

Roadside Units (RSUs) constitute a vital component of Vehicular Ad Hoc Network (VANET) due to their primary role in gathering vehicle information. However, as indicated in the literature, they are vulnerable to Denial of Service (DoS) attacks. Existing research has proposed several methods for detecting DoS attacks in RSUs, while subsequent steps such as mitigation and elimination remain unexplored. In this work, we propose a novel DoS Attack defense method for roadside units. We employ re-authentication to identify Sybil vehicles and discard the corresponding packets, thereby mitigating the attacks. Subsequently, we calculate the location of the actual malicious vehicle using a triangulation algorithm, in cooperation with traffic cameras and a cloud platform. This allows us to confirm the attacker and eliminate the DoS attacks. Simulation results demonstrate that our proposed method effectively defends against DoS attacks and successfully traces the attackers. Our method is a supplement to existing detection techniques. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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