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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sree, R. Sharmikha Raja, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Bharathi Salai Chennai600089 India

ISBN: (纸本)9798331508845

Strong protection against cyber threats is ensured by forensic analysis and cloud environment security being reinforced by the use of real-time threat detection and advanced encryption *** existing approach struggles to keep up with changing cyber threats and lack the strategic thinking skills necessary to address new security issues. To overcome these drawbacks, this work presents a novel method that maximizes security and forensic analysis in cloud-based settings by fusing blockchain technology, the game theory, and recursive Gated Recurrent Unit (GRU) models. The behavioral analytic capabilities of the recursive GRU models are especially helpful for detecting patterns and anomalies in security-related cloud logs, as evidenced by the UNW-NB15 dataset. Its flexibility is essential for identifying changing cyber threats and identifying anomalous user behavior. Using blockchain technology to create a decentralized, immutable ledger helps to mitigate data integrity issues that may arise from forensic investigation in cloud environments. This ledger guarantees the accuracy of data for forensic examinations, and smart contracts which come with built-in security features automate access controls and security issue remedies. Because blockchain is decentralized, it reduces individual points of failure, which improves the security of cloud infrastructure as a whole. By synchronizing security occurrences in real-time with the blockchain, the proposed connection creates a reliable and unchangeable record. Programmed intelligent agreements respond quickly to security incidents by issuing alerts or executing specified actions, as detected by the recursive GRU model. Through the use of continuous learning techniques, the model continuously adjusts to new data, improving its comprehension of both common and uncommon tendencies based on security incidents recorded on the blockchain. In cloud environments, this approach seeks to improve safety, openness, and effectiven

关键词： Cloud security

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A Reliable Approach for Automatic Grading of Diabetic Retinopathy from Fundus Images using Feature Extraction and Fusion 3

A Reliable Approach for Automatic Grading of Diabetic Retino...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Jasmine, R.Anita Ramla, M. Ramesh, M. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science Chennai Kattankulathur India

ISBN: (纸本)9798350369083

Diabetic Retinopathy (DR) is widely recognized as the primary cause of visual impairment worldwide. Early intervention is crucial in preventing irreversible vision loss. Ophthalmologists conventionally utilize fundus cameras to capture retinal images and detect indications of diabetic retinopathy, thereby facilitating prompt diagnosis. The progress made in computational intelligence has significantly enhanced our ability to identify, predict, and categorize DR. The focus of this research is to present a proficient framework for the DR grading. The framework proposed employs Laplacian filters to reduce undesirable image distortions and enhance the overall quality of the images. The methodology involves the utilization of transfer learning through the pre-existing VGG19 Convolutional Neural Network architecture to pull out features from diverse pooling layers. The amalgamation of extracted features from multiple layers results in the formation of feature vectors. These vectors are utilized as inputs for training various machine learning algorithms, with the objective of identifying the most effective model for evaluating the severity of DR. Significantly, the research provides a rationale for its forecasts, guaranteeing the transparency of the algorithm and comprehensibility of the model, thereby validating its reliability. The model's exceptional precision renders it a promising candidate for broad implementation in clinical identification and prediction of DR, potentially augmenting global patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approach for Intrusion Detection and Mitigation in IoT Environment: A Comprehensive Study

Deep Learning Approach for Intrusion Detection and Mitigatio...

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Arthi, R. Krishnaveni, S. Srm Institute of Science and Technology Computer Science and Engineering Chennai India Srm Institute of Science and Technology Computational Intelligence Chennai India

ISBN: (纸本)9798350337426

The Internet of Things (IoT) has gained importance over the last decade due to the exponential growth in digital communication systems and networks. The future society relies on connecting and communicating everything through the internet. However, the likelihood of attacks such as Distributed Denial-of-Service (DDoS) attacks, man-in-the-middle attacks, and other routing attacks is one of the key issues in the IoT environment. To arrive at a solution to this issue, it is important for academicians and researchers to integrate the existing information and statistics about this serious threat. To fill this gap, this paper presents a comprehensive survey of deep learning approaches for intrusion detection mitigation in IoT networks. First, it provides deeper insights into the type of malware in various IoT layers. Also, it discusses the various attacks in cloud and SDN layers. Next, it compares the intrusion detection and mitigation performance of various deep learning techniques in the cloud and SDN platforms. Then, it details the performance analysis of deep learning techniques for benchmark datasets. Finally, it elaborates on the research trends and future direction of intrusion detection in the IoT environment. © 2023 IEEE.

关键词： Internet of things

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Towards Smart Hybrid Feature Analysis and Deep neuroevolutionary Brain Tumour Prediction based on Transfer Learning 3

Towards Smart Hybrid Feature Analysis and Deep neuroevolutio...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Gowthamy, J. Suleka, C. Pereira, W Kenitawin Helena Kumar, P Monish SRM Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The brain tumors detected early and precisely is essential for giving patients a better chance of recovery. Magnetic Resonance Imaging are the leading method for problems abnormalities in the brain, however traditional diagnosis relies on manual analysis by medical professionals, which can diagnose brain tumors from MRIs can be lengthy, rely heavily on individual interpretation, and lead to mistakes. It introduces a new method for automatically finding brain tumors in MRIs using deep learning technology. Deep learning has revolutionized medical image analysis by enabling computers to learn complex patterns directly from data. This research leverages grayscale MRIs and employees deep learning algorithms architecture, Xception, to achieve high-performance computer-aided diagnosis. The Xception utilizes depth-wise separable convolutions, a technique that offers a balance between model accuracy, size, and computational efficiency. It examines the Xception model performs with transfer learning. In transfer learning, a deep learning model trained on a vast amount of data is adapted to a new task. The model is fine-tuned for the detecting brain tumors in MRIs and has the potential to make brain tumor diagnosis faster and more accurate compared to traditional methods. By automating tumor detection and reducing human subjectivity, deep learning-based systems improves early diagnosis and treatment planning for brain tumors. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Ensuring Data Security and Routing Efficiency in Warehouse Sensor Networks Using Wireless Blockchain technology 11th

Ensuring Data Security and Routing Efficiency in Warehouse S...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Anitha Rajakumari, P. Karthick, S. Mahapatra, Rajendra Prasad Parwekar, Pritee Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad India

ISBN: (纸本)9789819717231

The integration of wireless sensor networks (WSNs) and blockchain technology has gained significant attention in recent years due to its potential in addressing security and privacy concerns in various applications. In this paper, we propose blockchain technology with wireless-based secure warehouse system (BCWS2) that leverages wireless blockchain technology along with an efficient routing policy to ensure data integrity, authenticity, and confidentiality in a warehouse environment. The proposed system consists of a network of battery-powered wireless sensors deployed within the warehouse premises to monitor various environmental parameters such as temperature, humidity, and light levels. These sensors collect data and transmit it securely to a blockchain network using cryptographic techniques to prevent unauthorized access and tampering. The blockchain network, powered by smart contracts, acts as a decentralized ledger, ensuring the immutability and transparency of the collected sensor data. Each sensor node contributes to the blockchain by forming a consensus on the validity of the data through a distributed consensus protocol. This enables secure and tamper-proof storage of the collected data, eliminating the need for a central authority or trusted intermediary. In order to enhance routing efficiency within the warehouse sensor network, we present a highly efficient routing policy that leverages machine learning algorithms. Our proposed routing policy aims to optimize the path selection process, ensuring effective and streamlined communication within the network. The policy dynamically adjusts the routing paths based on the current network conditions, such as node congestion, battery levels, and signal strength. The proposed routing approach significantly enhances network performance, minimizes energy consumption, and guarantees prompt delivery of sensor data to the blockchain network. This system effectively tackles various challenges encountered in conventiona

关键词： Blockchain

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A Perspective to Cloud Computing in Healthcare Sector 2

A Perspective to Cloud Computing in Healthcare Sector

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Himanshu Punhani, Akash Srm Institute of Science and Technology Delhi-NCR Campus Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350371055

The medical services industry is on the cusp of another period driven by the union of distributed computing, man-made consciousness (simulated intelligence), and the Ayushman Bharat Wellbeing Record (ABHA) card drive. Distributed computing offers adaptability, adaptability, and cost-viability for medical care information the executives, while man-made intelligence holds enormous potential for customized medication, prescient investigation, and constant demonstrative help. The ABHA card, as the public computerized wellbeing identifier, gives a bound together stage to getting to and overseeing individual wellbeing records. This examination paper proposes an extensive structure for coordinating distributed computing, man-made intelligence, and ABHA to accomplish customized medical care. The paper will dig into the execution of this structure, tending to be specialized difficulties, information security contemplations, and moral ramifications. It will likewise investigate the possible effect of this methodology on working on tolerant results, decreasing medical services costs, and upgrading patient fulfilment. © 2024 IEEE.

关键词： ABHA ABHA Card AI Cloud Computing Data management Healthcare Security

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Computation of M-Polynomial and Topological Indices of Some Cycle Related Graphs

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IAENG International Journal of Applied Mathematics 2024年第8期54卷 1528-1539页

作者： Rajeswari, S. Parvathi, N. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

Topological indices provide valuable insights into various physical, chemical, and biological properties of molecules. Topological indices are correlated with various physicochemical properties of molecules, including boiling point, melting point, stability, distortion, strain energy, and ultimately biological activity. QSAR models utilize statistical techniques to establish quantitative relationships between these descriptors and the observed biological activity of compounds. By analyzing a dataset of molecules with known biological activities and their corresponding topological indices, QSAR models can predict the activity of new compounds based solely on their structural features. The M-polynomial provides information about the closed form of topological indices based on the degrees of the molecular graph. In this paper, we compute the M-polynomial of some families of graphs, tadpole, helm, gear, friendship, and fan graph. Further, we compute various topological indices for the above-mentioned graph in M-polynomial. © (2024), (International Association of Engineers). All rights reserved.

关键词： Molecules

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A Study on Design, Development and Deployment of Web Crawler Algorithms and Their Metrics

A Study on Design, Development and Deployment of Web Crawler...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Arthy, J. Raja, K. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Tamilnadu Ramapuram600089 India

ISBN: (纸本)9798350364828

Globally, the search engine is extremely important in reducing the difficulty of information exploration. An internet spider, bot, or program known as a web crawler is used by search engines to maintain an index and retrieve online sites in response to user queries. Web pages are really crawled in order to be explored, starting from the seed URL. In this study, the standard protocols of the literature survey were followed to describe the crawler mechanism also analyzed and compared several research publications to explain the crawler types, individual architectural types, policies, methodologies, metrics, techniques, and equipments for every category was covered. Additionally, the maximum crawler algorithms and the open-source crawler tools were listed and described. This paper gives insight into choosing the optimal crawler algorithm for search engine optimization and for various applications. © 2024 IEEE.

关键词： Web crawler

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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