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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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Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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International Journal of Information technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health Records Personalized healthcare Sentiment analysis Symptom

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SAT Problem Solving based on Hybrid Grey Wolf Genetic Algorithm 7

SAT Problem Solving based on Hybrid Grey Wolf Genetic Algori...

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7th International Conference on Intelligent Robotics and Control engineering, IRCE 2024

作者： Li, Ya Sun, Yu Tong Lv, Hong Yang Chen, Lei Qu, Tian Wei Yun, Hai Hong Heilongjiang Institute of Technology College of Computer Science and Technology Harbin China Institute for Advanced Ceramics School of Materials Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350352399

A hybrid algorithm based on grey wolf optimization algorithm, genetic algorithm, and heuristic information is proposed for solving SAT problems. The algorithm is an incomplete algorithm. To solve the problem that the retention of dominant individuals in the standard genetic algorithm depends on selection probability, the grey wolf optimization algorithm is introduced into the crossover operator to increase the probability of inheriting dominant individuals to new generation. At the same time, there is also a significant randomness in mutation operation, which makes the entire algorithm prone to falling into local optima and the convergence speed is too slow. Therefore, in mutation operator, select those variables whose values can satisfy more clauses, to jump out of local optima while accelerating the convergence speed of the algorithm. Through comparative experiments, it was found that the algorithm proposed in this paper can quickly solve satisfying problems, and the algorithm is efficient and stable. © 2024 IEEE.

关键词： Optimization algorithms

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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Diversity and Freshness-Aware Regret Minimizing Set Queries 29th

Diversity and Freshness-Aware Regret Minimizing Set Queries

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29th International computing and Combinatorics Conference, COCOON 2023

作者： Guo, Hongjie Li, Jianzhong Shen, Fangyao Gao, Hong Faculty of Computing Harbin Institute of Technology Heilongjiang Harbin China School of Computer Science and Technology Zhejiang Normal University Zhejiang Jinhua China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9783031491924

Multi-criteria decision-making often involves selecting a small representative set from a database. A recently proposed method is the regret minimization set (RMS) queries. It aims to rectify the shortcomings of needing a utility function in top-k queries and the overly large result size of skyline queries. However, the existing definition of RMS only ensures one result under any utility function, and do not consider the diversity and freshness of the returned results. In this paper, we define a strong regret set, which guarantees the utility value error of k data points under any utility function. Given this new definition, we propose two problems, namely the Minimum Size problem and the Max-sum Diversity and Freshness problem. Both proposed problems have been proven to be NP-hard. Correspondingly, we devise approximation algorithms for them, and analyze algorithms’ time complexities and the approximation ratios of the solutions obtained. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Approximation algorithms

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Graph Convolutional Networks for SEO: A Comprehensive Framework for Healthcare Information Ranking

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Journal of The Institution of Engineers (India): Series B 2025年 1-16页

作者： RR, Prianka M, Vedaraj R, Anto Arockia Rosaline Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R.M.D. Engineering College RSM Nagar Kavaraipettai Thiruvallur India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India

Internet’s remarkable surge, ubiquitous accessibility, and serviceability have increased users’ dependency on web services for fast search and recovery of wide sources of information. Search engine optimization (SEO) has become paramount in healthcare industries, which helps patients enhance and understand their health status based on their records. In the context of healthcare, it is more significant to improve search results from specific keywords related to clinical conditions, treatments, and healthcare services. So, this research work proposes a Graph Convolutional Network (GCN)-based Search Engine Optimization (SEO) algorithm for healthcare applications. The algorithm utilizes two distinct datasets: MIMIC-III Clinical Database and Consumer Health Search Queries to optimize search engine rankings for health related queries. Following data acquisition, data pre-processing is performed for better enrichment of analysis. The preprocessing steps involve data cleaning, data integration, feature engineering, and knowledge graph construction procedures to remove noisy data, integrate medical data with user search behavior, compute significant features, and construct knowledge graphs, correspondingly. The relation between the data entities is examined within constructed graph through link analysis. The pre-processed data including medical knowledge weights, content relevance scores, and user interaction signals are processed further on GCN model with Adam-tuned weights and bias for ranking healthcare data based on relevance score in response to user query using cosine similarity. The search relevance estimation indicators namely recall, precision, f1-score, and normalized discounted cumulative gain (NDCG) are computed to measure search optimization performance. The proposed GCN-SEO approach benchmarked its effectiveness over existing methods in optimizing web searches related to healthcare with a high performance rate of 95.75% accuracy and 48.25 s dwell time. This sho

关键词： Clinical research

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Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning

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Chinese Journal of Electronics 2022年第2期31卷 345-353页

作者： WANG Yueyue LEI Xiujuan PAN Yi School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences

Numerous microbes inhabit human body,making a vast difference in human health. Hence, discovering associations between microbes and diseases is beneficial to disease prevention and treatment. In this study,we develop a prediction method by learning global graph feature on the heterogeneous network(called HNGFL).Firstly, a heterogeneous network is integrated by known microbe-disease associations and multiple *** on microbe Gaussian interaction profile(GIP) kernel similarity, we consider different effects of these microbes on organs in the human body to further improve microbe similarity. For disease similarity network, we combine GIP kernel similarity, disease semantic similarity and disease-symptom similarity. And then, an embedding algorithm called GraRep is used to learn global structural information for this network. According to vector feature of every node, we utilize support vector machine classifier to calculate the score for each microbe-disease pair. HNGFL achieves a reliable performance in cross validation, outperforming the compared methods. In addition, we carry out case studies of three diseases. Results show that HNGFL can be considered as a reliable method for microbe-disease association prediction.

关键词： microorganisms global graph feature learning support vector machines patient diagnosis global structural information disease semantic similarity disease-symptom similarity HNGFL pattern classification graph theory microbe Gaussian interaction profile kernel similarity bioinformatics support vector machine classifier GraRep diseases learning (artificial intelligence) microbe-disease association prediction heterogeneous network Gaussian processes GIP kernel similarity

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Room-temperature waveguide-integrated photodetector using bolometric effect for mid-infrared spectroscopy applications

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光：科学与应用（英文版） 2025年第5期14卷 1300-1311页

作者： Joonsup Shim Jinha Lim Inki Kim Jaeyong Jeong Bong Ho Kim Seong Kwang Kim Dae-Myeong Geum SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-RoYuseong-GuDaejeon 34141Republic of Korea Department of Electrical & Computer Engineering Inha University100Inha-roMichuhol-guIncheon 22212Republic of Korea

Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)*** various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible *** leap beyond these limitations,the bolometric effect-a thermal detection mechanism-is introduced into the waveguide *** importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into ***,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with ***,our device exhibits a broadband responsivity of 28.35％/mW across 4030-4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10-7W/Hz0.5 at 4180 *** further demonstrate label-free sensing of gaseous carbon dioxide(CO2)using our integrated photodetector and sensing waveguide on a single *** approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.

关键词： applications temperature integrated room bolometric effect mid-infrared photodetector spectroscopy using

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Custom Transformer-Based Approach for Enhanced Bengali Automatic Speech Recognition 1

Custom Transformer-Based Approach for Enhanced Bengali Autom...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Deepa, R. Jayalakshmi, V. Thilakavathy, P. Manikandan, G. Surendran, R. SRM Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Ramapuram Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Anna University St. Joseph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350376517

ISBN: (纸本)9798350376517

Automated speech recognition (ASR) systems struggle with Bengali, which is the fifth most spoken language. Bengali has a varied morphology, many dialects, and limited high-quality annotated voice data. Traditional voice recognition techniques work well however struggle with Bengali phonetics and syntax. This method eliminates the need for large amounts of tagged data and the difficulties of differentiating sounds in different languages, both issues are addressed by this procedure. The research in this paper offers an Intellectual Bengali Speech Recognition System using Deep Learning Techniques (IBSRS-DLT). The model in this system uses transformer architecture and attention processes to manage Bengali speech pattern long-range dependencies. The IBSRS-DLT outperforms standard models in recognition accuracy using a pre-trained transformer model modified for phonetic and lexical correctness and fine-tuned on Bengali datasets. After training on a vast Bengali speech corpus, real-world datasets and rigorous simulations confirm the system. IBSRS-DLT can be used for automatic transcription, voice-controlled devices, and Bengali virtual assistants, such applications are efficient and accessible. The simulation showed a considerable boost in recognition speed and a decrease in keyword mistakes compared to current methods. This paper shows that deep learning can solve linguistic issues in ASR systems, enabling scalable regional language detection. © 2024 IEEE.

关键词： Deep learning

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