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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Empirical Analysis of Energy Harvesting and Clustering Methods for D2D Communication Systems

Empirical Analysis of Energy Harvesting and Clustering Metho...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Bura Vijay Kumar R. Vijayarangan V. Thirumurugan S. Sugumaran Raghunathareddy M V School of Computer Science and Artificial Intelligence SR University Warangal India CSE RVT Research Associates Chennai India CSE VISTAS Chennai India ECE Department Vishnu Institute of Technology Bhimavaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Unmanned Aerial vehicle (UAV)-based post-disaster communications face significant problems due to their energy consumption and operational range. Energy harvesting is used to overcome these problems by providing power for communication during disasters and extending the lifespan of wireless communication for networks. In order to expand the network's overall coverage and offer continuous connection during the disaster and post-disaster periods, Device-to-Device (D2D) communication and clustering methods were used. And Emergency Communication System (ECS) that uses the best Cluster Head (CH) technology to increase transfer energy effectiveness for long-term connectivity of the network. The clustering method and reinforcement learning in D2D connections are able to collect energy to extend the network lifespan with the help of UAV model. These strategies are predicted to increase the network's constancy during the emergency situations. The enhanced features with the use of the best CHs, and the path loss was significantly decreased. Hence, it reduces the computing complexity of network architecture to recover from natural disasters and also save numerous lives.

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Circle Search Algorithm with Convolutional Neural Network for Distributed Denial-of-Service Attack Detection in Software-Defined Networks

Circle Search Algorithm with Convolutional Neural Network fo...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： S. Padmakala Hassan M. Al-Jawahry R. Palanivel Y.M. Mahaboob John Arelli Madhavi Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE Mahendra College of Engineering Salem India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The Distributed Denial-of-Service (DDoS) attack is a crucial cybersecurity threat for Software-Defined Network (SDN). However, DDoS in SDN severely affect network availability which reduces the classifier performance. Hence, Circle Search Algorithm with Convolutional Neural Network (CSA-CNN) is proposed for detecting DDoS anomaly attacks in SDN. The DDoS anomaly dataset such as CIC-DDoS2019 is preprocessed through label encoding which changes categorical data to arithmetical data types through allocating single numerical labels to every class. Then, CSA is applied for feature selection which helps to efficiently detect malicious traffic patterns by exploring local and global search spaces and improves the detection accuracy. The CNN has ability to detect known and unseen attacks due to handling multidimensional data which increase the security of SDN. The CSA-CNN achieved better performance in terms of 99.97%, 99.95%, 99.94% and 99.98% of precision, sensitivity, f1-score and accuracy for CIC-DDoS2019 dataset when compared to Gated Recurrent Unit (GRU).

关键词： Accuracy Sensitivity Traffic control Network security Feature extraction Encoding Vectors Convolutional neural networks computer crime Software defined networking

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Selective Kernel Spatial Feature Extraction based Deep Learning Approach for Identification and Classification of Archaeological Sites

Selective Kernel Spatial Feature Extraction based Deep Learn...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Sri Lavanya Sajja Zaid Alsalami G Sunil Sunil Kumar V Ghazi Mohamad Ramadan Malla Reddy Engineering College for Women Secunderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Dept. of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India College of MLT Ahl Al Bayt University Karbala Iraq

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Deep Learning (DL) based identification and classification of archaeological sites are being used progressively from satellite images obtained from Geographic Information System (GIS). Even though, there were promising results from the existing researches, there is still need to enhance the classification with distinct features of the satellite images. The methods found challenging to accurately or partially identify an archaeological feature especially in a complex landscape due to lack of distinction between archaeological and non-archaeological features. For this, the geo-referenced satellite images are considered in this research, which are pre-processed using min-max normalization for efficient data analysis and to remove unwanted data. The Selective kernel spatial Feature Extraction (SFE) module is used to improve the feature extraction process where the spatial features are extracted. The extracted features are then classified using Fully Convoluted Deep Neural Network (FCDNN) for effective identification and classification of archaeological sites. The results showed that the proposed SFE-FCDNN achieved better classification accuracy with 98.83%, precision of 97.41%, and an error rate of 1.71 when compared to the existing methods such as Neural Network (NN), and Backpropagation Neural Network (BNN).

关键词： Deep learning Accuracy Data analysis Error analysis Heuristic algorithms Artificial neural networks Feature extraction Satellite images Kernel Geographic information systems

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Signal Quality Evaluation and Processing for QRS Detection in ECG based Smart Healthcare Systems

Signal Quality Evaluation and Processing for QRS Detection i...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Devendra Singh ShaikVaseem Akram T Sivakumar U. Prakash J Loyola Jasmine Chaithra K N Uttaranchal University Dehradun India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The next-generation wearable electrocardiogram (ECG) equipment requires signal processing with low battery usage in order to transmit signals when harmful rhythms are recognized and to capture signals when anomalous rhythms are discovered. The visual deflections that are frequently visible on an ECG make up the QRS complex. This research suggests a real-time QRS recognition and R point detection system that is extremely accurate and simple. The recommended ECG signal modification eliminates baseline wandering while also enhancing QRS intervals and controlling P and $T$ waves. In this work, the peaks and valleys of the converted signal were used to calculate the fiducial point for the QR. The R point could then be determined using four QRS waveform templates, and the initial categorization of cardiac rhythms could be completed simultaneously. On two benchmark datasets, the proposed method's effectiveness is shown. Positive prediction (+P) and detected sensitivity (Se) values for QRS are 99.82 and 99.81 percent, respectively, according to the standard. The outcome demonstrates that the method has a low processing complexity and that real-time software may be successfully executed on both an embedded system and a mobile device.

关键词： Visualization Heart beat Medical services Electrocardiography Real-time systems Software Internet of Things

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Predicting and Managing Medication Adherence Using Random Forest with Light Gradient Boosting Method and Mobile Health Data

Predicting and Managing Medication Adherence Using Random Fo...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Rana Veer Samara Sihman Bharattej R Haider Alabdeli Shashidhara K S M. Mumjitha Hirald Dwaraka Praveena Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Chemistry New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Electronics and Communication Engineering School of Engineering Mohan Babu University (Erstwhile Sree Vidya nikethan Engineering College) Tirupati India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent years, the importance of medication adherence is increased healthcare industry as prevalence of chronic diseases, like diabetes, hypertension, and asthma has improved. The traditional models failed in providing the correct dosage at correct time and at correct day. So, to overcome these limitations in this research, Random Forest- Light Gradient Boosting Method (RF-LightGBM) is proposed for predicting and managing medication adherence which is employed on Health and Retirement Study and consists of collection of health data which is gathered from the older adults in the United States. These data are pre-processed by using Synthetic Minority Over-sampling Technique - Edited Nearest Neighbours (SMOTE-ENN) and Natural Language Toolkit (NLTK) and optimal features are selected by Recursive Feature Elimination (RFE) that worked as wrapper for eliminating irrelevant attributes. The selected optimal features are classified by RF and LightGBM for distinguishing adherent and non-adherent patients and predicting medication adherence. Finally, RF- LightGBM is combined for associating the strengths of both RF and LightGBM which improved the accuracy and efficiency for classification and prediction. the proposed RF - LightGBM achieved better accuracy of 0.995, specificity of 0.984 and sensitivity of 0.992 when compared with existing Support Vector Machine (SVM).

关键词： Radio frequency Support vector machines Wireless communication Accuracy Sensitivity Soft sensors Forestry Boosting Older adults Retirement

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Ant Colony Optimization in MANET for Decreasing Energy Consumption with ML Approaches

Ant Colony Optimization in MANET for Decreasing Energy Consu...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Suresha P Gayatri Math Siddapuram Arvind M Bhaskar Naveen I G Department of Mechanical Engineering S.E.A College of Engineering and Technology Bengaluru India Department of Electronics & Communication Engg HKE society's B.V. Bhoomaraddi college of Arts science and commerce Degree college Bidar India Department of Computer Science & Engineering Hyderabad Institute of Technology and Management Telangana India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

To coordinate access from the active nodes, MAC protocols are used in ad hoc networks. Many media access control (MAC) protocols have been suggested for use in ad hoc networks, each with its own unique set of goals. The goal of many ad hoc networks is to increase the amount of time that its individual nodes, and by extension, the network, can function. Ad-hoc nodes are considered dead when their batteries run out, hence it is crucial to reduce power drain. Because to the nature of MANETs, in which nodes must depend on limited, portable power sources, energy efficiency is a significant barrier to their widespread implementation. In multi-hop systems, where nodes must additionally expend energy to route packets for other nodes and assure the connection of the network, energy saving is difficult. Since energy-efficient routing is a relatively new area of study, there are still many open questions that need to be investigated and no widely accepted standards in place. Several routing protocols used the shortest route as a criterion for path selection; however, this is inappropriate due to the topology's significant and unexpected variability and does not assume the most efficient use of energy. Simulation is the primary tool for testing the efficacy of several routing protocols in MANETs, including RIP, RIPng, PS-RIP, PSLLC-RIP, PS-RIPng, PSLLC-RIPng, FLBUDTCRIP, and ANFISBUDTCRIP. Exata 5.4 GUI network simulator and OPNET tools are used to analyse the efficiency of the suggested protocols. All the simulation results show \that the suggested protocols perform better than the current RIP protocols.

关键词： Wireless networks Authentication Media Access Protocol Routing Ad hoc networks Routing protocols Energy efficiency

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Levy Flight Strategy with Chicken Swarm Optimization Algorithm for Real-Time Anomaly Detection in Cybersecurity

Levy Flight Strategy with Chicken Swarm Optimization Algorit...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Mallubhotla Syamala SaiSree Subramanya S G Sripelli Jagadish S. Sureshkumar Mohammed Al-Farouni Mallareddy Engineering College for Women (UGC-Autonoumous) Hyderabad India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Kalasalingam Academy of Research and Education Krishnankoil India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

The cybersecurity field has witnessed several Intrusion Detection System (IDS) and Anomaly Detection, which are essential for identifying malicious activities in network traffic. However, growing volume and diversity of modern network attacks pose challenges, impacting the accuracy of these detection systems. This paper, Levy Flight strategy with Chicken Swarm Optimization Algorithm (LCSO) and Gradient-Boosted Decision Tree (GBDT) techniques achieve better accuracy. GBDT builds decision trees iteratively and helps in reducing bias and variance, leading to high accuracy models, especially with feature selected by using CSO. The CSO algorithm applied to various types of data, making it a flexible feature selection method for different cybersecurity. Initially data obtained from KDD-99 and NSL-KDD dataset and normalization is essential to prevent feature with large range from dominating the learning process. The feature selection using LCSO algorithm focusing on the most relevant features, helps improved efficiency achieve classification accuracy. The GBDT build better classifiers that handle both normal and anomalous pattern efficiently, especially with optimized feature set. The proposed LCSO-GBDT algorithm achieves better accuracy of 99.15% on KDD and 99.12% on NSL-KDD dataset when compared to existing techniques Deep Neural Network (DNN) and Bat Algorithm approach.

关键词： Accuracy Focusing Artificial neural networks NSL-KDD Feature extraction Classification algorithms Decision trees computer security Particle swarm optimization Anomaly detection

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Image Manipulation Detection and Classification Using SIFT and SURF with Support Vector Machine Classifier

Image Manipulation Detection and Classification Using SIFT a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Ghazi Mohamad Ramadan Zamen Latef Naser Sripelli Jagadish Rekha Phadke Muntather Almusawi College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary Pattern Texture Descriptor.

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A Reinforcement Learning Method in Cooperative Multi-Agent System for Production Control System

A Reinforcement Learning Method in Cooperative Multi-Agent S...

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Data science and Network Security (ICDSNS), International Conference on

作者： V. Malathy Hassan M. Al-Jawahry Madhura G K G. Suganya Rashmi P Department of ECE SR University Waranga India Department of Computers Techniques Engineering College of Technical Engineering The Islamic university Najaf Iraq Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Coimbatore India Inventuriz Labs Private Ltd Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Nowadays, in various domains including distributed control, telecommunication, robotics and economics to address the problems, Multi-Agent Systems (MAS) is used. To solve with preprogrammed agent behavior complexity of many tasks of these domains making it difficult. The multi- agent systems can limit problem of control in complex system production, to solve more efficiently. However, local optimization tendencies are often shown in iterative development algorithms. So, by using reinforcement learning and considering indicators of global key performance this paper presents a new method for cooperative multi-agent system. To cooperative order agents, for the purpose of this a central deep and double deep learning module its knowledge is being transferred. From the results, subsequent reinforcement learning using memory, the order's experience is stored and increases 50% of average mean cycle. In comparison of production control conventional methods, nonlinear production system and parameters of learning interdependencies between the characteristics are investigated and the performance is evaluated.

关键词： Production systems Job shop scheduling Reinforcement learning Production control Network security Complexity theory Manufacturing Optimization Robots Multi-agent systems

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Intrusion Detection in IoT Environment Using Stochastic Gradient Descent with Warm Restarts and Gated Recurrent Unit

Intrusion Detection in IoT Environment Using Stochastic Grad...

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Data science and Network Security (ICDSNS), International Conference on

作者： Revatthy Krishnamurthy Haider Alabdeli Rajani N Vankudoth Ramesh Bura Vijay Kumar Department of Artificial Intelligence & Machine Learning Rajalakshmi Engineering College Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE-DS CVR College of Engineering Mangalpalle India School of Computer Science and Artificial Intelligence SR University Warangal India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The rapid growth of Internet of Things (IoT) has attracted the cybercriminals which resulted in increase of cyber-attacks on IoT devices. If the cyber-attacks are undetected for a longer time, it causes serious service interruptions, identity protection threats. To handle these issues, this research proposes a novel intrusion detection system by employing Stochastic Gradient Descent with warm Restarts and Gated Recurrent Unit (SGDR-GRU) method. The data is normalized to obtain common scaled data by using z-score normalization technique and the feature selection is performed by Stochastic Gradient Descent with warm Restarts (SGDR). The SGDR is an optimization technique that escapes local minima, reduces overfitting and improves the performance by adjusting learning rate. Finally, classification of normal and anomaly data is carried out by Gated Recurrent Unit (GRU) that deals with gradient descent problem and captures long-range dependencies. The performance of the proposed model is evaluated on NSL-KDD dataset and achieved high accuracy 99.12%, precision 99.45%, recall 99.23% in identifying intrusion over IoT devices. The proposed method surpassed existing intrusion detection models such as stacked-deep polynomial network (SDPN), Hybrid Chicken Swarm Genetic Algorithm (HCSGA).

关键词： Performance evaluation Accuracy Intrusion detection Stochastic processes Transforms Logic gates Feature extraction Internet of Things Optimization Cyberattack

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