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Random Forest-Based Oppositional Henry Gas Solubility Optimization Model for Service Attack Improvement in WSN

Journal of The Institution of Engineers (India): Series B 2022年第3期103卷 939-950页

作者： Jeyalakshmi, S. Sekar, S. Ravikumar, S. Kavitha, D. Department of Information Technology SRM Valliammai Engineering College Chennai India Department of Computer Science and Engineering SRM Valliammai Engineering College Chennai India

A wireless sensor network (WSN) comprises self-organized and homogenous node sets referred to as sensor nodes. Every sensor node in the network contains few limitations on power consumption as well as resource. On the other hand, WSN is considered as the resource constricted network which is subjected to different attacks. However, WSN is greatly vulnerable to denial-of-service (DoS) attacks because it requires synchronization among various nodes. There are various types of DoS attacks situated at several layers. Several attacks on the data link layer caused energy loss due to a dead network. To improve the security of the wireless sensor networks, a mixed kernel extreme learning machine with a random forest-based oppositional Henry gas solubility optimization algorithm (MKELMRF-OHGSA) is developed to isolate and detect the attacks in the data transmission stage. In this paper, the approach for the effective identification of DoS attacks in the data link layer such as unfairness, resource exhaustion, collision and eavesdropping attacks is explained. The experimental simulation is conducted on the performance measures such as detection rate, network lifetime, packet delivery ratio accuracy, false alarm rate, and throughput to precisely isolate the attackers. The evaluation results describe that the proposed approach achieves an average detection rate of 98% when compared with other approaches. © 2022, The Institution of Engineers (India).

关键词： Denial-of-service attack

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Live college Bus Tracking and Route Mapping Using Internet of Things 2

Live College Bus Tracking and Route Mapping Using Internet o...

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Vincent, Bibin Sahu, Jestin Mathew, Cerlin Nair, Sachin S George, Sharon Biju Saleema, D. Providence College of Engineering Department of Computer Science Chengannur India

ISBN: (纸本)9781665493932

The proposed system works on GPS based tracking system which uses ReactJS to develop this application. This system displays the attendance report of the students who have boarded the bus, gives the estimated arrival time and live video feed to monitor the students for their safety. Beyond this feature it also offers the ability to create or update routes and places in the database. This system can be utilized by any travelling organization to get information about the vehicles' current locations. The user will be able to access the system using any web browser or through an Android Application. The system mainly uses Server module, Bus Unit module and End User Application module. The Server module is in charge of monitoring and recording the bus location. The Bus Unit module is in charge of tracking the bus's current location, recording the attendance of each student in the bus and provides it to server. The essential components of this module are internet-connected microcontroller, RFID and GPS modules. The end user application module provides user interfaces for both administrators and users. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Enhanced Security for IOMT Devices: Deep learning Techniques for Network Intrusion Detection 5

Enhanced Security for IOMT Devices: Deep learning Techniques...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Gopala Krishnan, D. Diviya, M. Jyoshika Shri, K. Subramanian, M. Amrita School of Computing Department of Computer Science and Engineering Chennai India St Joseph's College of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350379884

The increasing popularity of Internet of Medical Things (IoMT) devices, like wearable sensors, has greatly improved patient care by allowing continuous monitoring and real-time data transfer to the cloud. However, this progress also raises security concerns, as the data sent from these devices can be at risk of tampering or hacking during transmission. Keeping this sensitive medical data safe is crucial for patient safety. To address these issues, a fused CNN and LSTM are developed to check whether the data from wearable IoMT devices has been altered or attacked during transmission to the cloud. The CNN part of the model helps to pull out important features from the data, while the LSTM part looks for patterns to spot any signs of tampering. The model uses two convolutional layers each of which is followed by a MaxPooling layer. Our model, tested on a large dataset, achieved an accuracy of 97.06%, showing its ability to protect the integrity of transmitted medical data. © 2024 IEEE.

关键词： Network intrusion

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. Tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

The use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. The study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. This approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from GitHub to demonstrate the methodology. The sample is selected to reduce the complexity and to increase the performance. The dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. This improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP International Federation for Information Processing 2024

关键词： Snow

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ResMHA-Net:Enhancing Glioma Segmentation and Survival Prediction Using a Novel Deep Learning Framework

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computers, Materials & Continua 2024年第10期81卷 885-909页

作者： Novsheena Rasool Javaid Iqbal Bhat Najib Ben Aoun Abdullah Alharthi Niyaz Ahmad Wani Vikram Chopra Muhammad Shahid Anwar Department of Computer Science School of Engineering and TechnologyIslamic University of Science and TechnologyAwantipora192122India Faculty of Computing and Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia Research Groups in Intelligent Machines National School of Engineers of SfaxUniversity of SfaxSfax3038Tunisia Department of Electrical Engineering College of EngineeringKing Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of Electrical and Instrumentation Engineering Thapar Institute of Engineering and TechnologyPatiala147004Punjab Department of AI and Software Gachon UniversitySeongnam-si13120Republic of Korea

Gliomas are aggressive brain tumors known for their heterogeneity,unclear borders,and diverse locations on Magnetic Resonance Imaging(MRI)*** factors present significant challenges for MRI-based segmentation,a crucial step for effective treatment planning and monitoring of glioma *** study proposes a novel deep learning framework,ResNet Multi-Head Attention U-Net(ResMHA-Net),to address these challenges and enhance glioma segmentation ***-Net leverages the strengths of both residual blocks from the ResNet architecture and multi-head attention *** powerful combination empowers the network to prioritize informative regions within the 3D MRI data and capture long-range *** doing so,ResMHANet effectively segments intricate glioma sub-regions and reduces the impact of uncertain tumor *** rigorously trained and validated ResMHA-Net on the BraTS 2018,2019,2020 and 2021 ***,ResMHA-Net achieved superior segmentation accuracy on the BraTS 2021 dataset compared to the previous years,demonstrating its remarkable adaptability and robustness across diverse ***,we collected the predicted masks obtained from three datasets to enhance survival prediction,effectively augmenting the dataset *** features were then extracted from these predicted masks and,along with clinical data,were used to train a novel ensemble learning-based machine learning model for survival *** model employs a voting mechanism aggregating predictions from multiple models,leading to significant improvements over existing *** ensemble approach capitalizes on the strengths of various models,resulting in more accurate and reliable predictions for patient ***,we achieved an impressive accuracy of 73%for overall survival(OS)prediction.

关键词： Glioma MRI segmentation multihead attention survival prediction deep learning

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An Efficient way of Emotion and Gesture Recognition using Deep Learning Algorithms 5

An Efficient way of Emotion and Gesture Recognition using De...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Periyanayaki, M. Duraisamy, M. Duraisamy, Premkumar Natarajan, Yuvaraj KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Government Arts and Science College Department of Computer Science Tirupattur India

ISBN: (纸本)9781665493604

Researchers in psychology, computer science, linguistics, neurology, and allied fields have become more interested in a human-computer interface system for autonomous face recognition or facial expression recognition. An Automatic Facial Expression Recognition System (AFERS) has been suggested in this study. Three steps comprise the proposed methodology: face detection, feature extraction, facial expression recognition, and gesture recognition. Face detection's initial steps include YCbCr-color skin colour detection, illumination correction for uniformity on the face, and morphological procedures for maintaining the necessary face region. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. Automatic facial expression recognition, the third stage, entails straightforward. Using the AAM (Active Appearance Model) approach, the first phase's output is utilized to extract facial features such as the mouth, nose, and eyes. © 2023 IEEE.

关键词： Face recognition

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Artificial Neural Network Machine Learning Model for Malicious Action Detection and Prevention of MANET Assets 1

Artificial Neural Network Machine Learning Model for Malicio...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Mani, G. Prabakaran, R. University College of Engineering Department of Computer Science and Engineering Tamil Nadu Kancheepuram India Department of Electrical and Electronics Engineering Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9798350361780

The main objective of this paper is to create a novel architecture of a machine learning model to identify and detect the malicious attacks occur in MANET statically and dynamically. Earlier research works have proposed various secured routing protocols for malicious activity detection whereas they are all detecting the attacker after the malicious action created in the network. However, the problem is, the MANET needs a novel method to identify and detect the malicious action to prevent the network, data, and other properties of the network. Hence this paper proposed an Artificial Neural Network model for learning, diagnosing, and detecting abnormal activities present in the MANET, and eliminate the corresponding actions and the node from the network function. Three different dataset is created in the network before and after deploying the nodes in the network, routing table, and intermediate node information input to ANN for analyzation. The simulation is created in NS-2 software and the results are verified based on the network performance with respect to remaining energy, throughput, packet delivery ratio, and packet loss. The performance of the ANN is evaluated by comparing its output with the other earlier methods and prove its efficiency. © 2024 IEEE.

关键词： Network security

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Semantic Tree Based PPDP Technique for Multiple Sensitive Attributes in Inter Cloud

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SN computer science 2024年第6期5卷 722页

作者： Gadad, Veena Sowmyarani, C.N. Dayananda, P. Department of Computer Science and Engineering R V College of Engineering Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education

Digital devices and information systems have made data privacy essential. The collected data contains sensitive attributes such as salary, marital status and health history that need to be protected. Such data is exchanged or published to a third party using cloud infrastructure to perform various analyses, conduct research, and make critical decisions. Unauthorized users of the published data may violate privacy, notwithstanding the benefits. Data anonymization is one of the technique for achieving data privacy. Existing techniques consider single sensitive attribute and data is anonymized using generalization or suppression approaches. On observation, it is found that these techniques are less efficient since the collected data contains multiple sensitive attributes when anonymized using the same approaches leads to higher information loss and residue records. In this paper, multiple sensitive attributes are considered and the dataset is anonymized by constructing a semantic hierarchical tree it is further partitioned using the anatomy approach. Later, the partitions are stored in interclouds to achieve better privacy protection. Experiments are conducted to observe and analyze the computational performance, residue records and diversity percentage. The results obtained prove that the proposed technique is efficient when compared to the existing ones. © The Author(s) 2024.

关键词： Anonymization Data privacy Information loss Multiple sensitive attributes Privacy threats

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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