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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Mendanha, Serena Gudibandi, Keerthi Reddy Hajarathaiah, Koduru Gudibandi, Bharat Reddy Enduri, Murali Krishna School of Computer Science and Engineering VIT-AP University Amaravati India School of Electronics Engineering VIT-AP University Amaravati India Srm University Department of Computer Science and Engineering AP India

ISBN: (纸本)9798331523923

The central nervous system is impacted by multiple sclerosis (MS), a chronic neurological condition that causes significant cognitive and physical deficits. Better disease management and prompt intervention depend on early and precise detection. Effective diagnosis of MS is hampered by a number of factors, such as small datasets, significant clinical presentation variability, difficult feature selection, model generalization problems, and integration of multimodal data such as magnetic resonance imaging and genetic markers. Several machine learning (ML) models are assessed in this study in order to predict the development of clinically isolated syndrome (CIS) to multiple sclerosis. Using clinical and magnetic resonance data from patients with CIS at risk of developing MS, we evaluate the effectiveness of support vector machines (SVM), K nearest neighbor (KNN), decision trees (DT), random forests (RF), logistic regression (LR), Gaussian naive Bayes (Gaussian NB) and XGBoost (XG). Evaluation is performed using performance criteria including F1 score, recall, precision, and precision. According to our research, Random Forest has the best prediction accuracy, which makes it a potentially useful tool for helping doctors diagnose and treat MS patients early. Notwithstanding these developments, issues like model interpretability and data scarcity still exist. In order to improve diagnosis precision, future research will concentrate on enhancing these models by integrating deep learning methods, genetic markers,, and more advanced imaging modalities. © 2025 IEEE.

关键词： Magnetic resonance imaging

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Applications and Associated Challenges in Deployment of Software Defined Networking (SDN)

Applications and Associated Challenges in Deployment of Soft...

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International Conference on Emerging Trends and Applications in Artificial Intelligence, ICETAI 2023

作者： Baniya, Pashupati Agrawal, Atul Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9783031567278

SDN, a rising technology within the realm of Internet of Things (IoT), has been increasingly well-received in recent times. This article presents a summary of SDN along with its different elements, advantages, and difficulties. The paper aims to provide practical solutions for introducing OpenFlow into commercial routers without hardware modifications and extending the integration of OpenFlow with legacy control protocols and control planes. In addition, the paper presents a refactoring process for migrating traditional network applications to OpenFlow-based ones, focusing on the security challenges and techniques of open technologies like SDN, OpenROADM, and SDN-based Mobile Networks (SDMN). The document also examines the advantages and possible uses of SDMN in enhancing network adaptability, streamlining network administration, and bolstering network security. The article also discusses O-RAN network innovations and difficulties, such as AI and ML workflows that are made possible by the architecture and interfaces, security concerns, and, most importantly, standardization issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer's Classification

GAN-Alz: Synthetic Data Generation for Multiclass Alzheimer'...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Nair, Rekha R Babu, Tina Ramasamy, Gayatri Singh, Tripty Yuan, Xiaohui Alliance University Alliance School of Advanced Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwavidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India University of North Texas Denton Department of Computer Science and Engineering TX United States

ISBN: (纸本)9798350385199

Accurate detection of skin cancer, particularly melanoma, is crucial for effective treatment and patient survival. This study explores the use of Convolutional Neural Networks (CNNs) enhanced by Generative Adversarial Networks (GANs) for skin cancer detection. CNNs have shown remarkable performance in image classification tasks but often require extensive labeled datasets, which are scarce in medical domains. To address this, GANs are employed to generate synthetic skin lesion images, augmenting the dataset and improving the model's generalization capability. The proposed approach integrates a pre-trained CNN architecture fine-tuned with a combination of real and GANgenerated images, enhancing the network's ability to distinguish between benign and malignant lesions. Experimental results demonstrate that the inclusion of GAN-augmented data improves the model's accuracy by 7%, achieving an overall accuracy of 94%, with precision and recall rates of 92% and 93%, respectively. These findings suggest that the GAN-augmented CNN model offers a promising solution for automated skin cancer screening © 2024 IEEE.

关键词： Generative adversarial networks

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Edge AI: A survey

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Internet of Things and Cyber-Physical systems 2023年第1期3卷 71-92页

作者： Singh, Raghubir Gill, Sukhpal Singh Department of Computer Science University of Bath Bath United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom

Artificial Intelligence (AI) at the edge is the utilization of AI in real-world devices. Edge AI refers to the practice of doing AI computations near the users at the network's edge, instead of centralised location like a cloud service provider's data centre. With the latest innovations in AI efficiency, the proliferation of Internet of Things (IoT) devices, and the rise of edge computing, the potential of edge AI has now been unlocked. This study provides a thorough analysis of AI approaches and capabilities as they pertain to edge computing, or Edge AI. Further, a detailed survey of edge computing and its paradigms including transition to Edge AI is presented to explore the background of each variant proposed for implementing Edge Computing. Furthermore, we discussed the Edge AI approach to deploying AI algorithms and models on edge devices, which are typically resource-constrained devices located at the edge of the network. We also presented the technology used in various modern IoT applications, including autonomous vehicles, smart homes, industrial automation, healthcare, and surveillance. Moreover, the discussion of leveraging machine learning algorithms optimized for resource-constrained environments is presented. Finally, important open challenges and potential research directions in the field of edge computing and edge AI have been identified and investigated. We hope that this article will serve as a common goal for a future blueprint that will unite important stakeholders and facilitates to accelerate development in the field of Edge AI. © 2023 The Authors

关键词： Machine learning

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Real-Time Identification of Fake Currency Notes Using YOLOv9 and GELAN 8

Real-Time Identification of Fake Currency Notes Using YOLOv9...

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8th IEEE International Conference on Computational system and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kumar, Vivek Pandey, Mukesh Juyal, Devesh Raj, Anand Pandey, Pratibha Quantum University Department of Computer Science & Engineering Roorkee India School of Computer Science University of Petroleum and Energy Studies Dehradun India Uttaranchal University Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331505462

In the past many years, counterfeit currency has been proliferating on a large scale in India, which is not just increasing black money but also destroying the value and reputation of Indian currency in the global market. The actual circulation of counterfeit notes across India remains unknown, posing a significant threat to the financial security of ordinary citizen. Hence, this research proposes utilizing YOLOv9 technology, a real time advanced object detection model, to develop a machine-learning system for detecting counterfeit Indian currency. Here we are focusing on the Rs 500 note, and using its images, we are creating a robust model to detect fake currency by training the model with both real and fake denominations of Rs 500. In this, we are using the latest version of YOLO, which is v9, and its latest technology, GELAN, which improves the accuracy and efficiency of the model to detect the fake currency notes from the real one using image processing Techniques. © 2024 IEEE.

关键词： Machine learning

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The Future of Work: Robotic Process Automation and its Role in Shaping Tomorrow’s Business Landscape

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SN computer science 2025年第2期6卷 1-19页

作者： Bhardwaj, Vivek Yadav, Shveta Kaur, Navjeet Anand, Darpan School of Computer Science and Engineering Manipal University Jaipur Jaipur India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

In the new era of automation, Robotic Process Automation (RPA) has emerged as a powerful suite of tools for automating mundane, repetitive, rule-based, and structured tasks using software bots without disrupting existing infrastructure. Due to its significant potential, RPA has garnered considerable attention in the business world, being both easier and cheaper to implement while enhancing efficiency and productivity. This study begins by examining the characteristics of an RPA system and suitability of processes for RPA. This study delves into the role and impact of RPA in modern businesses, highlighting its transformative potential. It also explores the latest trends in RPA research, providing insights into cutting-edge developments and innovative applications. Furthermore, the paper addresses the challenges faced in implementing RPA and offers strategies for overcoming these obstacles, aiming to enhance the efficiency and effectiveness of RPA deployments in various business contexts. Lastly, RPA software bots were developed to quickly and accurately extract data from various invoices. The results show that, when implemented correctly, an RPA system is both easy to deploy and scalable, while maintaining high accuracy. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial intelligence Business processes Intelligent automation Robotic process automation (RPA) Software bots

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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computer Vision for Agricultural Automation - Algorithmic Solutions for Crop Yield Predictions

Computer Vision for Agricultural Automation - Algorithmic So...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Ramachandran, A. Ganesh Saravanan, S.K. Bhanumathi, M. Sangeetha, M. Fernandez, F. Mary Harin Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats Department of Computers Science and Engineering Chennai India Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Computer Engineering Department Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350388602

computer vision is defined in this study and one of the applications demonstrated is crop yield prediction in agricultural automation. Here we have emphasized on its use for computer vision for the agricultural domain. Using multispectral images that can be obtained from drones satellites and pairs with ground truths of average crop yield, weather conditions and parts of the soil treated with practices performed by an agronomist the proffered methodology would take advantage of the uniqueness of the concept to predict crop production. Further, elaborate machine learning methods such as line regression, decision trees, Random Forest, Support Vector Machine and CNN models are applied in order that the ultimate selection of the model would be contingent on the accuracy in relation to a crop yield. The DL based approaches as seen majority of this work and particularly the CNN exhibited improved results over regression models since there are more of classification than regression;In addition DL models are more appropriate for spatial relations and variations characteristic of crop data. Besides that, data fusion methods which map the accuracy of the images, weather and fields data develops a more accurate forecast as proven by the above crop modeling that involves multidimensional data. While transferring on fresh test data, it also demonstrates that the trained models have the capability to use the collected data in order to not only provide intelligent advices to the farmers on resource goodwill and crop productivity that will pave way for better results. Most importantly therefore for the hear of it the research show that with computer visions and machine learning the agricultural process can be reinvented this is how food security can be improved as the environment evolves. © 2024 IEEE.

关键词： Agricultural automation computer vision Crop yield prediction Deep learning Machine learning Multispectral imagery

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