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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Vijiyakumar, K. Muthunagai, R. Bhanusree, A. Nikhilsowrabh, K. School of Computing Srm Institute of Science and Technology Department of Computing Technology Chenngalpet district Kattankulathur India Manakula Vinayagar Institute of Technology Department of Electrical & Electronics Engineering Puducherry India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331510022

Automated detection of defects in medical imaging has emerged as a crucial domain within various diagnostic applications, especially with MRI regarding tumor detection. 'While accuracy in the detection of tumors is important in providing vital information regarding tissues which have deviated from their normal state, it's even more important for effective treatment planning'. Currently, the diagnosis of tumors from brain MRI scans relies on human assessment, which is infeasible for large image data and error-prone based on judgments of humans. Thus, there is a pressing need for robust and efficient automated classification systems that can overcome these bottlenecks. The core idea of our project is the development of the most advanced tumor detection methods, which saves precious time for radiologists but at a very high accuracy and, thus, reduces mortality levels in patients. In our approach, we have used several CNNs - VGG19, ResNet50, and MobileNetV2, which we have applied through transfer learning for detecting tumors from brain MRIs. We also incorporate into our model an ensemble of these varied CNN architectures that leverages their strengths since the nature and diversity of tumors are intricate. In fact, the ensemble method is really intended to boost the wide-ranging accuracy and strength of the procedure in the detection of tumors. The proposed system utilizes deep learning techniques in combination with image classification to accurately and efficiently identify cancerous cells in brain MRI scans. After that, the web application, developed using Flask, will provide a user-friendly interface for uploading MRI images and then instantaneously return predictions to a doctor on the potential presence of a tumor. © 2024 IEEE.

关键词： Convolutional Neural Networks (CNNs) Deep Learning EnsembleMethod Flask Web Application MRI Imaging TransferLearning Tumor Detection

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NeurDB: an AI-powered autonomous data system

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Science China(Information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Gray-Hole Attack Minimization in IoMT with 5G Based D2D Networks

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Computer Systems Science & engineering 2022年第9期42卷 1289-1303页

作者： V.Balaji P.Selvaraj Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Reliable transmission is vital to the success of the next generation ofcommunications technologies and Fifth Generation (5G) networks. Many sensitive applications, such as eHealth and medical services, can benefit from a 5G network. The Internet of Medical Things (IoMT) is a new field that fosters themaintenance of trust among various IoMT Device to Device (D2D) modern technologies. In IoMT the medical devices have to be connected through a wirelessnetwork and constantly needs to be self-configured to provide consistent and effi-cient data transmission. The medical devices need to be connected with sophisticated protocols and architecture to handle the synergy of the monitoring ***, one of the commonly used algorithms in D2D communication is the Optimized Link State Routing protocol (OLSR). The OLSR is considerably good ateffectively utilizing the bandwidth and reserving the paths. One of the majorattack against the OLSR is the Node isolation attack, also known as the Gray holedenial of service attack. The Gray hole attack exploits the vulnerabilities presentwith sharing the topological information of the network. The attackers may usethis topological information to maliciously disconnect the target nodes from theexisting network and stops rendering the communication services to the victimnode. Hence, considering the sensitivity and security concerns of the data usedin e-Health applications, these types of attacks must be detected and disabledproactively. In this work, a novel Node Authentication (NA) with OLSR is proposed. The simulation experiments illustrated that the proposed protocol has anexcellent Packet Delivery Ratio, minimal End-End delay, and minimal Packet losswhen compared to the Ad-hoc On-Demand Distance Victor (AODV) protocol andthe proposed authentication scheme was able to protect the OLSR protocol from anode isolation attack.

关键词： 5G AODV D2D IoMT OLSR security issues

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Cyberbullying Advisory Model 15

Cyberbullying Advisory Model

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Dhulipala, Sai Kowshik Jaswanth, Vungarala Ragunthar, T. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

In the current digital environment, cyberbullying remains a significant threat to online safety and well-being. This study explores the technological advancements and strategies utilized in the project, providing compelling evidence of its effectiveness in reshaping the landscape of cyberbullying recovery. To tackle this urgent issue, we introduce the "Cyberbullying Advisory," an innovative AI-powered solution. Unlike traditional technologies, our platform is specifically designed to offer a dedicated space for cyberbullying victims across different online platforms, allowing them to share their stories and begin a journey towards emotional healing. Notably, it goes a step further by providing legal guidance to assist users in navigating the complexities of online harassment. Incorporating technologies such as Natural Language Processing (NLP), Natural Language Understanding (NLU), and Deep Learning, our models utilize task-specific datasets to implement key features like Toxicity Level Detection, Empathetic Response, and Intent Recognition. By leveraging the BERT Model for improved user interaction, these technologies enable real-time empathetic responses and personalized legal advice, revolutionizing the user experience in combating cyberbullying. The integration of AI-driven analysis and supervised learning techniques ensures accurate identification and categorization of toxic content, allowing users to receive tailored support. The uniqueness of our project lies in the seamless integration of these components, emphasizing precision through the use of task-specific datasets. By providing victims with empathetic and context-aware responses, our platform serves as a steadfast ally, offering assistance to those impacted by cyberbullying across diverse digital platforms. © Grenze Scientific Society, 2024.

关键词： Phishing

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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

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Computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Efficient protection of golden nutri cereal implementing enhanced disease identification injecting hybrid split-attention mechanism through Novel SANDNES Mechanism

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Multimedia Tools and Applications 2024年第38期83卷 85499-85521页

作者： Divya, A. Sungeetha, D. Ramesh, Sundar Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur Chennai India

Millets, often referred to as "Nutri Cereals," hold significant potential for enhancing future food availability. Over the past 50 years, advancements such as the use of fertilizers, insecticides, and controlled irrigation have markedly increased the productivity of pearl millet crops worldwide. According to the 2020 review report by the Directorate of Millets Development, 693 million hectares of agricultural land are dedicated to pearl millet cultivation. However, managing crop diseases remains a substantial challenge for farmers. Effective crop disease management is crucial for sustainable agriculture, yet traditional algorithms often struggle with accurate detection and computational inefficiency especially in the case of golden cereal termed as "Pearl Millet". Our study introduces a novel approach for disease detection in pearl millet crops using aerial imagery, named Split-Attention Networks with Disentangled Nonlocal and Edge Supervision (SANDNES). This method leverages advanced neural network architectures to enhance disease detection accuracy and efficiency. Our comprehensive numerical evaluations demonstrate that SANDNES outperforms traditional U-Net and U-Net + Nonlocal baseline models Comprehensive evaluations demonstrate SANDNES' exceptional performance, achieving an impressive average F1 score of 89.04%, significantly surpassing other methods in accuracy and efficiency. Specifically, SANDNES achieved notable performance improvements, with F1 scores of 94.88% for healthy crops, 85.96% for honeydew, 82.97% for smut, and 92.69% for ergot. This method leverages split-attention mechanisms for precise feature localization, with Disentangled Nonlocal and Edge Supervision enhancing contextual reasoning and boundary delineation. SANDNES superior computational efficiency and high accuracy make it suitable for real-time precision agriculture applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024

关键词： Crops

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Plant Disease Detection and Classification using Convolutional Neural Networks (CNN) 15

Plant Disease Detection and Classification using Convolution...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sreenivasan, Mahalakshmi Selvaraj, P. Department of Computing Technologies Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (纸本)9798331300579

Farmers and producers may suffer significant financial losses due to plant diseases. Effective control of plant diseases requires early identification and categorization of the illnesses. However, traditional plant disease detection techniques are frequently labor- and time-intensive. Plant disease detection has found a possible collaborator in deep learning and machine learning. Consequently, a novel deep-learning technique is proposed for classifying and diagnosing plant illnesses. It uses a transfer learning methodology built on a convolutional neural network architecture consisting of 5 convolutional, 2 fully- connected, and 3 max pooling layers. The algorithm has a 98% accuracy rate when tested on a publicly accessible dataset of images of plant diseases. The CNN model was trained with the PlantVillage dataset, which is available on Kaggle. From the results and a comparison with previous works, it is evident that the chosen approach performed well in classifying plant diseases with expected accuracy. © Grenze Scientific Society, 2024.

关键词： Plant diseases

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Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

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Computer Systems Science & engineering 2023年第8期46卷 2141-2157页

作者： J.Prabakaran P.Selvaraj Department of Networking communications Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia Department of Computing Technologies Faculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational *** this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and ***,the CT images are captured and transmitted to the fog node through IoT *** the fog node,the image gets modified into a convenient and efficient format for further *** encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating ***,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning *** different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature *** softmax of heterogeneous base classifiers assists to make individual *** a meta-classifier,the ensemble approach is employed to obtain final optimal *** predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the *** proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class *** experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

关键词： Intelligent health care cloud computing fog computing ensemble learning RNN-LSTM

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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Computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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Data Encryption in 6G Networks: A Zero- Knowledge Proof Model 8

Data Encryption in 6G Networks: A Zero- Knowledge Proof Mode...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Khamesra, Aryan Selvaraj, P. Singh, Isha Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350376425

This work presents an innovative algorithm demonstrating the effectiveness of zero-knowledge proofs (ZKPs) in network security. By integrating Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) for key generation, the algorithm showcases their applicability in enhancing security measures within 6G networks. It highlights the utility of ZKPs in bolstering data privacy and security by enabling entities to validate knowledge without compromising sensitive information. The algorithm shows its capability to ensure robust communication security through comprehensive simulations, thereby laying the groundwork for dependable next-generation communication infrastructures. © 2024 IEEE.

关键词： Ciphertext

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