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Self-moving Window with Long-Short Term Memory based Anomaly Detection in Edge-Assisted IoT Environments

Self-moving Window with Long-Short Term Memory based Anomaly...

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

作者： Yerrolla Chanti Myasar Mundher Adnan R. Renuga Devi Lakshmanan M Arelli Madhavi School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram India Department of EEE Nitte Meenakshi Institute of Technology Bengaluru India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Internet of Things (IoT) systems is widely used in many applications which generate huge amounts of sensory signals that involves to signify the state of systems. However, due to changes in IoT environment impact the detection of anomalies accurately which lead to risk of losing information. To overcome this limitation, a Self-moving Window with Long Short-Term Memory (SW-LSTM) is proposed for accurate detection of anomalies. The self-moving window improved the ability of LSTM to identify subtle anomalies that evolve over time, leading to more accurate detection of unusual patterns or device behavior in IoT environments. Initially, the data about attacks and malicious nodes are acquired from the UNSW-15 dataset and preprocessed by score normalization technique. Then, the optimal set of features are selected by Particle Swarm Optimization (PSO) algorithm. Finally, the proposed SW-LSTM model detects the anomalies in edge assisted IoT network effectively. The experimental results of proposed method achieved accuracy of 93.4% which is greater when compared to existing detection approaches such as Recurrent Neural Network (RNN), Convolutional Neural Network (CNN).

关键词： Accuracy Recurrent neural networks Image edge detection Computational modeling Internet of Things Convolutional neural networks Object recognition Particle swarm optimization Long short term memory Anomaly detection

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Ant Colony Optimization based Medical Image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, Victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Predictive Analysis Using Modified Inception Net and Support Vector Machine for Forecasting Copra Yield in Agriculture

Predictive Analysis Using Modified Inception Net and Support...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Kavitha M G Raviprakash M L Kassem Al-Attabi B. Sathyavani S meenakshi Sundaram Department of Computer science and engineering Kalpataru Institute of Technology Tiptur India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

One of the major economic sectors in various states is copra agriculture, with a significant portion of its global production coming from countries such as India, Indonesia, and Sri Lanka. However, the severity of copra issues can vary depending on factors such as moisture levels, fungal presence, and the quality of the copra, leading to imbalanced data and potential overfitting in copra image analysis. Threshold-based segmentation is involved to identify different segments of copra based on factors like moisture content. Higher moisture content may indicate areas prone to fungal growth or poor drying conditions, while segments with uniform colour and texture may signify high-quality copra. The proposed approach involves using the Modified Inception Net (MIN) for feature extraction and Support Vector Machine (SVM) for hyperparameter tuning. SVM handles the kernel function in nonlinear and high-dimensional spaces, enhancing its capabilities and mitigating overfitting. However, the MIN-SVM combination may require significant computational resources, particularly during training and parameter tuning. The proposed MIN-SVM method achieves promising performance metrics, including a Mean Absolute Error (MAE) of 0.071, Mean Square Error (MSE) of 0.0125, Matthews Correlation Coefficient (MCC) of 0.620 and $R^{2}$ value of 0.9410. This performance is compared to existing methods such as Back Propagation Algorithm (BPA), K-means and SVM.

关键词： Support vector machines Measurement Backpropagation Image segmentation Moisture Production Feature extraction

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Fault Classification in Photovoltaic Arrays Using Convolutional Neural Networks with L2 Regularization

Fault Classification in Photovoltaic Arrays Using Convolutio...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Pradeep Kumar S. L. Umasankar Muntather Almusawi Pothireddy Surekha Srinivas Aluvala Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Electrical and Electronics Engineering S.A.Engineering College Poonamallee Tiruvallur India The Islamic university Najaf Iraq Department of Computer Science and Engineering GRIET Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal India

Fault detection in photovoltaic (PV) arrays is of paramount importance, because the output power is maintained at its highest value and the efficiency of the system is prolonged. In the past, there were issues of output accuracy of the detection method in complex situations such as PS and high impedance faults. This paper proposes a method for diagnosing PV failures based on the combination of Hybrid Transformer- Convolutional Neural Network (CNN) Architecture framework that is used for the purpose. Proposed method implies an extension of transformers that is capable to capture long-range dependencies of images, and CNNs to extract spatial features. This is because it performs in a higher way which is to achieve better fault classification performance. A contrast with the extant techniques that deal with limited few scenarios of faults, we have faced up to five different faulty scenarios, PS included and have devised the maximum power point tracking (MPPT). The results of this experimental work proved that the implemented hybrid method is much better than the previous methods regarding fault detection, and succeeded in having the accuracy of 88.4% compared to the other methods; and it is a promising one for betterment of the fault diagnosis in the photovoltaic systems.

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Real-Time Face Recognition Attendance System Based on Video Processing

Real-Time Face Recognition Attendance System Based on Video ...

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

作者： Rajath Kumar Y S Sindhu D Shashikala S V Arpitha K Chaithra K N Department of Computer Science BGS Institute of Technology Adichunchanagiri University Bengaluru India Department of Information Science BGS Institute of Technology Adichunchanagiri University Bengaluru India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The advent of big data and the commercial value of facial recognition technology have created a bright future for the technology, with a large market potential. This article proposes a face recognition participation framework based on continuous video processing. The article explores four aspects of the framework: the accuracy of facial recognition in offline recordings, the accuracy of facial recognition in continuous video processing, the dropout rate of the face recognition participation framework with continuous video processing, and the interface settings of the face recognition participation framework with continuous video processing. By analyzing these aspects, the article proposes a framework for participation based on facial recognition technology and conducts research on the framework with continuous video processing. Experimental results show that the accuracy of the video facial recognition framework can reach 82%. Compared with the traditional registration method, the face recognition participation framework can reduce the dropout rate by more than 60%. The dropout rate of missing classes has also been significantly reduced, which can effectively address the problems of students missing classes and leaving classes early.

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Contrastive Loss Function Siamese Network for Change Detection on Remote Sensing Images

Contrastive Loss Function Siamese Network for Change Detecti...

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

作者： N. Naga Saranya Ahmad Alkhayyat Shreyas A V Raviprakash M L T. Jayachandran Department of Computer Applications Saveetha College of Liberal Arts & Science SIMATS Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Civil Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of ECE Nandha Engineering College Erode India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

To observe the changes in the earth surface is increasing due to development of navigation software applications and also mainly prediction purposes for agriculture purpose and environmental hazards. To overcome the problem one of the most crucial technological choices for effectively identifying changes in observations in remote sensing photos is Change Detection (CD). Applications for CD are numerous and include mapping disasters, environmental monitoring, urban planning and land use study. On the other hand, the CD applications face significant hurdles due to the often-occurring class imbalance issue. In order to tackle this problem, a new type of Contrastive loss function Siamese network (CLFS-Net) that integrates the links between scenes and foregrounds to yield global-aware features for effective change detection. The CLFS-Net improve symbiotic connection learning between scene and foreground while simultaneously learning contextual relationships. The proposed CLFS-Network gained in terms of accuracy as 90%, precision as 95% and recall as 91% and f1 score as 95% respectively.

关键词： Symbiosis Accuracy Navigation Urban planning Network security Feature extraction Software Robustness Sensors Remote sensing

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Resource Allocation for Ultra-Dense 5G and Beyond Networks Based on Proximal Policy Optimization 3

Resource Allocation for Ultra-Dense 5G and Beyond Networks B...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Husseyn, Muhamed Nagarathna, P. Rajasekar, S.S. Varsha, R. Venkatasubramanian, R. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Computer Science Bengaluru India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Dhanalakshmi Srinivasan College of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798331508456

In recent years, resource allocation in Ultra Dense Networks (UDNs), which consist of Small Base Stations (SBSs), plays a crucial role in enhancing the efficiency and capacity of the network. However, significant challenges such as severe interference, inefficient resource utilization and congestion are introduced in the network due to the high density of SBSs. To overcome these challenges, a policy-based reinforcement learning method, Proximal Policy Optimization (PPO) is proposed, which continuously learns and adapts the network conditions by adjusting power levels and resource allocations, ensuring maximum Spectral Efficiency (SE) and Energy Efficiency (EE). Firstly, a 5G UDN downlink framework is developed, which includes a Macro Base Station (MBS). This MBS is integrated with a server, namely Mobile Edge Computing (MEC), which performs resource allocation among distributed SBSs and User Equipment (UEs). Furthermore, PPO is employed to train the MEC, which acts as an agent and interacts with the whole UDN environment, to make better decisions by improving its policy through reinforcement learning updates. The proposed PPO outperformed DQN by achieving 800bps/Hz in SE and 700bps/J in EE respectively. © 2025 IEEE.

关键词： Resource allocation

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Genetic Algorithm based Task Scheduling in High-Performance Cluster Computing

Genetic Algorithm based Task Scheduling in High-Performance ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： R. Renuga Devi Mohammed Fallah Madhura G K Mrinmoy Aich B. Aishwarya Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram 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 Oracle Texas USA Department of Computer Science and Engineering (CS) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In recent years, the demand for increased computational power has driven advancements in both High-Performance Computing (HPC) systems and multi-core technology. As these technologies play an important role in enhancing the power computation capabilities of different resources, traditional approaches for task scheduling in HPC had faced challenges like inefficient resource allocation and high task waiting time. Therefore, this research proposes Genetic Algorithm (GA) which refines the core scheduler for each task and enhances the configuration of task scheduling strategy. In core task scheduling, GA is utilized for minimizing average scheduling time by optimally allocating tasks across computing nodes. The GA operates through evolutionary processes by including population initialization, selection, crossover, and mutation which iteratively refined task allocation solutions. The proposed GA effectively reduced task waiting time, improved system throughput and achieved better results in terms of resource utilization (90.27%) when compared with existing Particle Swarm Optimization (PSO) respectively.

关键词： Accuracy Processor scheduling High performance computing Computational modeling Reinforcement learning Dynamic scheduling Throughput Real-time systems Resource management Genetic algorithms

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Resource Allocation for Ultra-Dense 5G and Beyond Networks Based on Proximal Policy Optimization

Resource Allocation for Ultra-Dense 5G and Beyond Networks B...

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

作者： Muhamed Husseyn P Nagarathna S. S. Rajasekar Varsha R R. Venkatasubramanian Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Al Diwaniyah Iraq Department of Computer Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial intelligence and Machine Learning Bannari Amman Institute of technology Sathyamangalam India Department of Computer Science and Engineering Dhanalakshmi Srinivasan College of Engineering Technology Mamallapuram India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

关键词： Base stations Multi-access edge computing 5G mobile communication Spectral efficiency Reinforcement learning Interference Resource management Servers Optimization Signal to noise ratio

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Weed Recognition Using Image Patches Based Global Hybrid Attention with Densenet-169 Model

Weed Recognition Using Image Patches Based Global Hybrid Att...

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Network, Multimedia and Information technology (NMITCON) International Conference on

作者： Hima Bindu Valiveti Muntather Almusawi E G Satish Srinivas Aluvala E S Challaraj Emmanuel Department of ECE Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Dept. of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India School of Computer Science and Engineering SR University Warangal India Department of Life Sciences Kristu Jayanti College Bengaluru India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

Weeds are one of the main problem in agriculture that affect the crop yield. Accurate classification and recognition is major challenges in weeding because of same visual characteristics between plants and weeds. To solve this problem, in this research patch based image recognition is integrated with the Deep Learning (DL) approach to improving the classification and recognition of weeds. The weed image data are collected from the Deepweeds dataset. For pre-processing Enhanced Super Resolution Generative Adversarial Network (ESRGAN) is utilized for change the image size and then generated to small patches. Selection of relative important patches are performed by Fast Fourier Transform (FFT) and Laplacian Filter (LF) are trained to the DL model. The proposed Global Hybrid Attention mechanism with Densenet-169 model (GHA-Densenet-169) employed for classification of weeds with respective classes. The evaluation results of the proposed method using performance metrics are Accuracy, Recall, Precision and F1-score. The proposed approach attained high accuracy of 99.28%, recall of 99.26%, precision of 99.27% and f1-score of 99.25% which is greater than existing deep learning models such as Densenet-201, VGG-SVM, Adapting MobilenetV2, Resnet-SVM, and Swin Transformer and Two-stage Transfer Learning (ST-TSTL).

关键词： Deep learning Adaptation models Visualization Image recognition Accuracy Laplace equations Fast Fourier transforms Computational modeling Feature extraction Transformers

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