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Efficient Multimodal Biometric Recognition for Secure Authentication Based on Faster Region-Based Convolutional Neural Network

Efficient Multimodal Biometric Recognition for Secure Authen...

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

作者： Ponugoti Kalpana Surendar Rama Sitaraman Sunil S Harakannanavar Zaid Alsalami S. Nagaraj Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad India Anna University Chennai India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Information Technology Karpagam College of Engineering Coimbatore India

ISBN: (数字)9798350372892

ISBN: (纸本)9798350372908

In recent years of technology, securing the personal information is an important thing to avoid financial loss, criminal attacks. So, in this regard demand for security regulations, biometric recognition technology, and information security have been increased tremendously. To overcome the security issues in this research, Multimodal Biometric recognition system was implemented by using a proposed method called as Faster Region-based Convolutional Neural Network (FRCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method implemented on SDUMLA-HMT dataset that contains the 3 biometric traits - face, finger vein, and fingerprint. In data preprocessing stage by using the TopHat and Soble filtering method the quality of the image is improved by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-means techniques. CNN which is significantly used to extract the features of the images and it also can be viewed as an automatic feature extractor. In classification stage, FRCNN is used as a classifier that achieved high accuracy of 99.53%, when compared with all other existing methods namely VGG-16, IrisConvNet, SVM and residual networks.

关键词： Support vector machines Accuracy Image recognition Filtering Face recognition Noise Fingerprint recognition Feature extraction Convolutional neural networks Residual neural networks

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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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Stock Price Prediction using LSTM and TLBO

Stock Price Prediction using LSTM and TLBO

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

作者： Kishore Kumar M Veena R C Ila Chandana Kumari. P Gandla Shivakanth Karunakara Rai B Department of CSE CMR Technical Campus Hyderabad India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Computer Science of Engineering Hyderabad Institute of Technology and Management Telangana India Dept of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The stock market's inherent complexity and volatility make accurate predictions difficult. Complex patterns in unstructured data may be simulated and analyzed with the help of Deep Learning. It is possible to employ deep learning models for a wide variety of purposes, including but not limited to image recognition, audio recognition, and natural language processing (NLP). As a result, everyone with a stake in the market is always trying to make educated guesses about what the stock price will do next. We used many deep learning techniques, including Long Short-Term Memory, to model our problem (LSTM). To successfully predict the closing prices of three distinct markets, an Extreme Learning Machine (ELM) model is combined with a variety of metaheuristic optimization techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Symbiotic Organism Search (SOS). These algorithms are helpful in creating ELM network weights and biases in the most effective way possible. The Teaching and Learning Based Optimization (TLBO) paradigm underpins most of the models and methodologies now in use for feature selection. To that end, this study sets out to identify the most effective machine learning model for making such predictions. The primary objective is to develop a smart technology that can predict the movement of stock market values based on financial time series and news headlines. Investors would be able to make more informed decisions based on the binary results of the proposed method. Processing time, error rate, and accuracy in forecasting the direction of stock price movement for five companies across sectors are used to evaluate the performance of the proposed LSTM-TLBO model.

关键词： Deep learning Symbiosis Image recognition Education Time series analysis Predictive models Prediction algorithms

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning

Optimizing Inventory Management using Demand Forecasting bas...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： V. Pavithra Haayder M. Abbas Manjula B M P. K. Chidambaram Jothilakshmi S Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Information Technology Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF).

关键词： Accuracy Computational modeling Stacking Demand forecasting Inventory management Predictive models Market research Feature extraction Imputation Tuning

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Convolutional Neural Network Based Glaucoma Detection and Classification using Optic Disc Localization

Convolutional Neural Network Based Glaucoma Detection and Cl...

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

作者： Mahesh B Neelagar T. C. Manjunath Raj Kumar Patra Sripelli Jagadish Debarshi Mazumder Department of ECE Dayanand College of Engineering Bengaluru Visvesvaraya Technological University Belgavi Karnataka India Department of Computer Science and Engineering CMR Technical Campus Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Glaucoma in the retina is considered a significant cause of irreparable vision loss, and automated glaucoma detection on fundus images has seen the development of several approaches recently. Although early diagnosis and treatment can prevent possible blindness, manually segmenting the optic disc depends on the availability of a large number of labeled samples for the training phase. The proposed method utilizes U-Net for segmentation based on labeled and unlabeled data for prediction and data enhancement with a large number of feature channels in up sampling. This approach can improve overall performance by considering pixel-level and image-level labels during network training and encoding the data train, differentiating U-Net from Convolutional Neural Network (CNN). Classification is performed using CNN for diagnosing glaucoma, evaluated on a large training dataset. The obtained results demonstrate that the proposed U-Net model achieves better accuracy, reaching 0.99 on the Drishti dataset. These results ensure accurate segmentation and classification performance compared to other existing methods such as CNN and Multi-layer Neural Network.

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Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine

Intrusion Detection System in Mobile Cloud Computing Using B...

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

作者： Haider Alabdeli Nagendar Yamsani Anitha D Chaithra K N G. Bindu The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations; hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively.

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An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal

An Automated Sleep Stage Classification for Healthcare Monit...

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

作者： Sasi Kumar Gurumoorthy D T Chethana M Ramesh B.P Upendra Roy C Sapna Kumari Department of Computer Science and Engineering J.J.College of Engineering and Technology Trichy India Department of Computer Science & Engineering GITAM School of Technology GITAM University India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering Channabasaveshwara Institute of Technology Tumkur India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy.

关键词： Support vector machines Adaptation models Time-frequency analysis Sleep Statistical analysis Feature extraction Brain modeling

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An Efficient Anomaly Detection using Generative Adversarial Networks based on Minimax Loss Function

An Efficient Anomaly Detection using Generative Adversarial ...

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

作者： Layth Hussein Jayapal Lande Biswaranjan Routray S meenakshi Sundaram V K S K Sai Vadapalli Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering Centurion University of Technology and Management Paralakhemundi Odisha Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India Department of IT S R K R Engineering Bhimavaram India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Recently, Artificial Intelligence (AI) is used as a vital tool for describing characteristics of information in the process of data mining and patterns. In data mining, an Anomaly Detection (AD) is an essential part which helps to determine behavior within the data and helps to detect network intrusion. The existing AD in network traffic has identified outliers in unbalanced datasets and difficult to train the model. Therefore, this paper introduces Generative Adversarial Networks (GAN) based on Minimax Loss Function for detecting anomalies in network traffic. Initially, this paper uses Neural Simulation Language-Knowledge Discovery in Databases (NSL-KDD) dataset which contains attacks and are preprocessed by Principal Component Analysis (PCA) to reduce the dimensionality. After that, these features are processed using the Information Gain (IG) algorithm for eliminating irrelevant features and then anomalies are detected by the proposed GAN-Minimax loss function which effectively identifies outliers, handles imbalanced data and trains the model effectively. The proposed GAN-Minimax resulted in high accuracy as 99.92% when compared with existing K-Nearest Neighbor (KNN) method.

关键词： Accuracy Clustering algorithms Telecommunication traffic Nearest neighbor methods Generative adversarial networks Feature extraction NSL-KDD Artificial intelligence Anomaly detection Principal component analysis

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Joint Channel Estimation and Feedback in Multiple-Input Multiple-Output Systems by using Pilot Feedback Network with Adaptive Compression Scheme 3

Joint Channel Estimation and Feedback in Multiple-Input Mult...

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

作者： Mohameed, Haeedir Biradar, Vidyadevi G. Murugan, K. Subramaniam, Sakthivel Koilraj, Thomas College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India Bannari Amman Institute of Technology Department of Electronics and Communication Engineering Sathyamangalam India Sona College of Technology Department of Computer Science and Engineering salem India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

Nowadays, Massive Multiple-Input Multiple-Output (MIMO) has become important in 5G which enhances channel capacity by developing large antenna arrays at the Base Station (BS). Traditional approaches had faced several challenges which include high pilot overhead and increased computational complexity. Therefore, this research proposes Pilot Feedback Network with Adaptive Compression Scheme (PFnet with Adaptive Compression Scheme) for Channel Estimation (CE) and feedback in MIMO systems. Initially, data is generated from COST 2100 model. Then, a massive MIMO system with OFDM subcarriers and N-t antennas are considered. For channel estimation, the Least Squares (LS) estimator with interpolation is employed because of its low complexity by making it suitable for scenarios where computational efficiency is prioritized. However, to enhance estimation accuracy, a deep learning-based Channel Estimation and Feedback Network (CEFnet) is integrated by improving the robustness of estimation. Unlike conventional deep learning-based CSI compression methods like CsiNet, PFnet directly compresses pilot signals rather than CSI by reducing overhead. The proposed Adaptive Compression Scheme further optimizes feedback efficiency by dynamically adjusting the compression ratio based on the Signal-to-Noise Ratio (SNR). The proposed PFnet with Adaptive Compression Scheme achieved better results in terms of Normalized Mean Square Error (NMSE) when compared with existing PFnet. © 2025 IEEE.

关键词： Least squares approximations

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A Denoising Convolutional Auto Encoder with Bi-LSTM for Corrupted ECG Signals Reduction

A Denoising Convolutional Auto Encoder with Bi-LSTM for Corr...

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

作者： Zaid Alsalami E G Satish M. PonniBala J. Rajalakshmi C. Sushama 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 Velalar College of Engineering and Technology Erode India Dept. of CSE School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The IoT based scheme e-health uses some measurable device for to monitor the health condition of our body. The long-term measurable system like cardiovascular, electrocardiogram (ECG) are significant methods to diagnose the patient's diseases. The drawbacks of this measurable system such as high computational cost, consume more time for training and ECG signals contained some noise and corrupted signals. To addresses this kind of problem, the Denoising Convolution Auto Encoder (DCAE) with Bidirectional Long Short-Term Memory (Bi-LSTM) method is implemented. The DCAE contains encoder and decoder part to convert the ECG corrupted signals into compressed form. Bi-LSTM attached to encoder part that carries the input signals and trains that. Bi-LSTM carries double layers, which makes the training process easily. This proposed method tested on MIT-BIH databases. The advantages of this method, auto encoder easily convert the corrupted signals without using any parameters and reduce computation cost. Bi-LSTM reducing the training process, time consumption and it consisted of double layers, which increase the compression process of signals fastly. The developed DCAE-Bi-LSTM gives better output rate of 97.85% which is better than other existing methods like CNN-LSTM and SVM.

关键词： Training Support vector machines Convolution Computational modeling Noise reduction Noise Electrocardiography Decoding Timing Noise measurement

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