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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A fine-tuned transformer model for brain tumor detection and classification

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Multimedia Tools and Applications 2025年第15期84卷 15597-15621页

作者： Srinivas, B. Anilkumar, B. devi, NLakshmi Aruna, V.B.K.L. Andhra Pradesh Vizianagaram535005 India Electronics and Communication Engineering GMR Institute of Technology Andhra Pradesh Razam532127 India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Razam532127 India Electronics and Communication Engineering V R Siddhartha Engineering College Andhra Pradesh Vijayawada520007 India

The identification and classification of brain tumors from medical images is a challenging task, which plays a crucial role in treatment planning. In recent times, the transformer models earned greater attention in image classification tasks because of their tendency to learn complex and spatial interconnections in medical images. In this study, a novel fine-tuned transformer model was developed by combining the benefits of a data-efficient image transformer (DeiT) and the firefly algorithm (FA) for the precise classification of tumors. This framework utilizes the transformer's self-attention and feed-forward layers to learn and capture complex patterns and high-level features. It employs the Firefly fitness solution to fine-tune the DeiT architecture by updating its hyperparameters, thereby maximizing the system performance and reducing training loss. The novelty of the paper lies in its proposal and implementation of a fine-tuned transformer model specifically tailored for the detection and classification of brain tumors. By leveraging the capabilities of transformer architectures, such as the data-efficient image transformer (DeiT), the proposed model can effectively capture intricate spatial dependencies and complex patterns within brain MRI images. The experimental analysis utilizes the benchmark brain tumor MRI database from the Kaggle source and the developed model achieved greater classification accuracy of 99.7%. In addition, a comprehensive comparative analysis was performed with existing models to demonstrate the robustness of the proposed work. This intensive analysis verifies that this model facilitates the accurate and timely detection of brain tumors in medical diagnosis. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image classification

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Indian Sign Language Interpretation using Skeletal Features and LSTM Networks 2

Indian Sign Language Interpretation using Skeletal Features ...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kankaria, Kunal Samarth, Adya Rawoorkar, Prajwal Oak, Keyur Patil, Shreya Dixit, Bharati School of Computer Science and Engineering Pune India School of Electronics and Communication Engineering Pune India

ISBN: (纸本)9798350360523

Indian Sign Language (ISL) is a standardized and commonly used medium of communication for the deaf and dumb community in India. Despite an enormous deaf and dumb community, there are only about 300 certified ISL interpreters. This necessitates robust automated sign language interpretation systems. For the classification of gestures, we develop LSTM and Bi-LSTM networks trained on skeletal features extracted via MediaPipe. Augmentation techniques are used to enhance model performance. Furthermore, a uniform frame sampling method is applied to reduce computational complexity and overfitting. We achieve an accuracy of 97.92% with the proposed system, thus demonstrating its high usability. © 2024 IEEE.

关键词： Long short-term memory

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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A Coherent Analytical Model to Strengthen Vehicular communication in VANET Using Droptail Priority Queue

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Kumar, Satyam Sinha, Bam Bahadur Khare, Priyank Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India

People’s demand for vehicles has been increasing day by day over the last few decades. A survey tells us that over 50,000 vehicles run on the roads per day. Such a large number of vehicles causes traffic. A survey tells us that over 1200 road crashes occur everyday in India out of which 25% result in deaths. Most of these accidents occur due to a lack of communication between the vehicles. This has led to the attraction of researchers towards the Vehicular Ad Hoc Network (VANET) ensuring road safety by establishing an Intelligent Transportation System (Malhi in Comput Secur 89:101664, 2020). In VANET, due to the frequent movement of vehicles and rapid variations in topology, the communication link between vehicles may break. This paper analyzes these problems and comes up with a novel approach to overcome these and maintain the performance of the protocol. In order to assess the performance metrics for the proposed protocol, an analytical model has been created. Simulations have been carried out to validate it and have been compared against similar state of comparative methods. © The Institution of Engineers (India) 2025.

关键词： Vehicular ad hoc networks

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Advanced Breast Cancer Detection Using Spatial Attention and Neural Architecture Search (SANAS-Net)

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SN computer science 2025年第1期6卷 1-12页

作者： Souza, Melwin D. Ananth Prabhu, G. Kumara, Varuna Department of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore India Department of Electronics and Communication Engineering Moodlakatte Institute of Technology Kundapura India

This paper introduces a novel spatial attention neural architecture search network (SANAS-Net), which incorporates a spatial attention mechanism to enhance the model’s ability to focus on critical regions within mammograms. By utilizing multi-head attention within transformer blocks, the model captures diverse spatial relationships and interactions between features. Positional embeddings are incorporated into the transformer blocks, further enriching the model’s capacity to understand global spatial context. Extensive experiments were conducted to validate the proposed methodology. SANAS-Net was shown to successfully identify attentive networks that demonstrate high performance in distinguishing malignant from benign breast cancer cases. The model achieved a test accuracy of 89.95%, significantly surpassing the performance of previously proposed algorithms for breast cancer detection using mammography images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Breast cancer detection Deep learning Mammography Neural architecture search Spatial attention

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An Optimized Deep-Learning-Based Low Power Approximate Multiplier Design

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computer Systems science & engineering 2023年第2期44卷 1647-1657页

作者： M.Usharani B.Sakthivel S.Gayathri Priya T.Nagalakshmi J.Shirisha Department of Electronics and Communication Engineering Er.Perunal Manimekalai College of EngineeringKonneripalliHosur635117India Department of Electronics and Communication Engineering Pandian Saraswathi Yadav Engineering CollegeSivagangaiTamilnadu630561India Department of Electronics and Communication Engineering R.M.D Engineering CollegeGummidipundiTamilnadu601206India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGuntur522502India Department of Electronics and Communication Engineering Malla Reddy Engineering CollegeHyderabadTelangana500015India

Approximate computing is a popularﬁeld for low power consumption that is used in several applications like image processing,video processing,multi-media and data *** Approximate computing is majorly performed with an arithmetic circuit particular with a *** multiplier is the most essen-tial element used for approximate computing where the power consumption is majorly based on its *** are several researchers are worked on the approximate multiplier for power reduction for a few decades,but the design of low power approximate multiplier is not so *** seems a bigger challenge for digital industries to design an approximate multiplier with low power and minimum error rate with higher *** overcome these issues,the digital circuits are applied to the Deep Learning(DL)approaches for higher *** recent times,DL is the method that is used for higher learning and prediction accuracy in severalﬁ***,the Long Short-Term Memory(LSTM)is a popular time series DL method is used in this work for approximate *** provide an optimal solution,the LSTM is combined with a meta-heuristics Jel-lyﬁsh search optimisation technique to design an input aware deep learning-based approximate multiplier(DLAM).In this work,the jelly optimised LSTM model is used to enhance the error metrics performance of the Approximate *** optimal hyperparameters of the LSTM model are identiﬁed by jelly search ***ﬁne-tuning is used to obtain an optimal solution to perform an LSTM with higher *** proposed pre-trained LSTM model is used to generate approximate design libraries for the different truncation levels as a func-tion of area,delay,power and error *** experimental results on an 8-bit multiplier with an image processing application shows that the proposed approx-imate computing multiplier achieved a superior area and power reduction with very good results on error rates.

关键词： Deep learning approximate multiplier LSTM jellyﬁsh

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Development of a diacritic-aware large vocabulary automatic speech recognition for Hausa language

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International Journal of Speech Technology 2024年第3期27卷 687-700页

作者： Abubakar, Abdulqahar Mukhtar Gupta, Deepa Vekkot, Susmitha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics & amp Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Research on voice recognition for African languages is limited due to the scarcity of digital resources for training and adaptation, despite its broad usefulness. The Hausa language, spoken by almost fifty million inhabitants in West and Central Africa, is an example of a linguistic domain that has not been thoroughly studied. The Hausa language employs diacritics, which are symbols located above alphabetical characters to convey further information. By removing diacritics, the number of homographs increases, making it difficult to distinguish between similar words. This paper presents a study on speech recognition in the Hausa Language, specifically focusing on diacritized words. The study utilises the state-of-the-art wave2vec2.0 and Whisper deep learning architecture models, for transcribing audio signals into corresponding Hausa text. According to the results obtained in the study, the Whisper-large deep model emerged as the best, achieving a word error rate of 4.23% representing a considerable improvement of 43.9% when compared to the existing state-of-the-art model for Hausa language speech recognition. Additionally, the Whsiper-large model demonstrated a diacritic coverage of 92%, precision of 98.87%, with a diacritic error rate of 2.1%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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