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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Ashreetha, B. Sumanth, Upputuru Reddy, Lebaka Naveen Kumar Prasad, K.D.V. Srinivas, Kale Naga Venkata Gowda, Dankan V. Department of Electronics and Communication Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Mohan Babu University Andhra Pradesh Tirupati India Symbiosis Institute of Business Management Hyderabad India Pune India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India

ISBN: (纸本)9798350361155

The precision medicine in oncology provided a novel era where the patients have better chances of being cured and improved outcomes if they adopted the personalized treatment strategies. The core part of this change is the utilization of the ML (machine learning) algorithms which showcases good specificity as compared to humans in making neoplasm diagnoses. This research paper will be exploring an application of ML approaches within precision medicine area for oncology patients and will be trying to fill-up the gap between big genome data and decision making processes in the clinical area. This work proposes a complete study which investigates the usefulness of several strategies of ML in diagnosis under cancer with the help of the detailed dataset that consists of genomic and clinicological profile of patients. In our study we investigated several types of ML algorithms covering decision trees, neural networks as well as support vector machines. Every algorithm underwent strict test and validation for its diagnostic power to be identified. Our approach inaugurated the use of ROC curves, precision-recall curves, as well as survival analysis to objectively evaluate model performance, along with feature importance rankings which allowed for identification of biomarkers as well as genetic signatures known to be predictive of specific cancer subtypes and treatment responses. ML work is seen to show a substantially higher sensitivity, specificity and accuracy compared to conventional diagnostic methods. Having ML integrated with precision medicine, makes the screening of cancer in early stages and the accurate diagnosis possible. Additionally, it provides the opportunity to devise personal treatment plans depending on the various genetic tumor types. Besides that, the results from survival analysis illustrated that these ML-based prognostic models can be used for accurately forecast patient outcomes. This has an implication of not only better management of existing treat

关键词： Diagnosis

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Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes 5

Development of Cancer Drug Targets and Potential Anti-Cancer...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Balamurugan, K.S. Ramesh, T. Sathish Kumar, P.J. Surendran, R. Tamilvizhi, T. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets. © 2024 IEEE.

关键词： Mammals

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An Empirical Study of Covid-19 Effect on Health Care Workers' Career Development

An Empirical Study of Covid-19 Effect on Health Care Workers...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Acar, Kutay Orman, Gunce Keziban Turhan, Sultan Nezihe *** Electronic Publishing and Communication Services Inc Istanbul Turkey Galatasaray University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350324457

The COVID-19 pandemic has affected the lives and health of many people. Among them, health care workers indeed played the biggest role. In this work, we investigate the health care workers job applications, and the job postings change with the time series analysis perspective. The dataset is provided by ***, which is the foremost job-searching company in Turkey. Since the data set includes job applications over every region of country, the results reveal important facts about health care workers in Turkey. We aim to study the possible effect of vaccination on health care workers job search decisions. © 2023 IEEE.

关键词： COVID-19 Healthcare Data Healthcare Workers Healthcare Workers' Career Development Healthcare Workers' Wellbeing

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A Semi-Supervised Deep Learning Approach for Intrusion Detection and Classification for the Internet of Things

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Biomedical Materials and Devices 2025年 1-17页

作者： Uthradevi, G. Thiruvasagam, P. Mythili, S. Manoj, S. Oswalt Department of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Electrical and Electronics Engineering Chennai Institute of Technology Tamil Nadu Kundrathur Chennai 600069 India Department of Biomedical Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Kuniyamuthur 641008 India

Intrusion Detection Systems (IDS) are essential for protecting networks from cyber threats and ensuring the security of critical infrastructures. This paper introduces an innovative hybrid model that combines Deep Belief Networks (DBNs), Principal Component Analysis (PCA), and Support Vector Machines (SVM) with adversarial learning to enhance the detection of both known and emerging cyber threats. The proposed model achieves a strong balance of accuracy, efficiency, and scalability, making it highly effective for real-world applications. By incorporating adversarial learning, the system can detect zero-day exploits—previously unseen attacks—and adapt to evolving threats. This is achieved by training the model on adversarial samples, which improves its resilience to variations in attack patterns. SVM further enhances the model’s ability to classify known and unknown threats with high precision. The model was rigorously tested on two benchmark datasets, NSL-KDD and CICIDS2017, achieving outstanding results. It demonstrated a high accuracy rate of 99.73% on NSL-KDD and a low false positive rate of 0.55%, while on CICIDS2017, it achieved a false positive rate of 0.73%. The model also outperformed existing approaches in precision, recall, and F1-score, particularly in detecting complex attack types such as R2L (Remote-to-Local), U2R (User-to-Root), and Botnet attacks. Additionally, PCA reduced the feature space by 40%, significantly lowering the model’s inference time to just 9.0 ms per sample. This makes the model highly suitable for real-time intrusion detection in high-traffic network environments, where speed and efficiency are critical. Overall, this hybrid model strengthens cybersecurity defenses and contributes to building resilient infrastructures. By effectively detecting and mitigating cyber threats, it supports the creation of safer, fairer, and more inclusive digital communities. The integration of adversarial learning and dimensionality reduction ensures rob

关键词： Adversarial learning Cyber security Deep belief networks Dimensionality reduction Feature extraction IDS Networks PCA SVM

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An optimized framework for implementation of smart waste collection and management system in smart cities using IoT based deep learning approach

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5033-5040页

作者： William, P. Patil, Jaikumar M. Panda, Sunita Venugopal, Anita Vidyullatha, Pellakuri Kumar, Nellore Manoj Jandwani, Aman Department of Information Technology Sanjivani College of Engineering SPPU Department of Computer Science and Engineering Shri Sant Gajanan Maharaj College of Engineering SGBAU Department of Electrical Electronics and Communication Engineering GITAM School of Technology IT Unit Dhofar University Department: Computer Science and Engineering Koneru Lakshmaiah Education Foundation Department of Mathematics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Research Task

Nation’s rapid urbanization growth and economic development, waste generation has significantly increased. An important environmental concern on a global scale is waste management. In a majority of the world’s nations, including India’s metropolitan centers, organic waste management is a major problem. Therefore, it is necessary to create a productive system that will either eliminate or significantly decrease this issue. It will assist us in effectively maintaining a hygienic environment, and natural atmosphere. The implementation of smart cities requires an effective garbage collection system. In the given paper, we propose a smart waste collection and management (SWCM) system centered on the Internet of Things (IoT) architecture. A deep learning (DL) approach for classifying waste is being used. The proposed framework includes the use of smart bins for the collection of garbage, the categorization of that waste into several groups, and the management of smart waste. For this study, data from smart garbage cans were gathered. The data was normalized during preprocessing. The efficacy of the proposed system is assessed and compared with conventional as well as existing recent studies based on performance metrics such as classification accuracy (%) and power consumption (%). The results of the study reveal that the proposed approach is impactful in the smart collection and management of waste. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： And hygienic environment Deep learning Internet of things (IoT) Smart cities Smart waste

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Recognition of De Novo Drug Design for Targeted Chemical Libraries through Optimization Techniques 5

Recognition of De Novo Drug Design for Targeted Chemical Lib...

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5th International Conference on Smart electronics and communication, ICOSEC 2024

作者： Balamurugan, K.S. Navaneethakrishnan, Sundara Rajulu Srinivasulu, Senduru Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Samayapuram Campus Tiruchirappalli Trichy India Sathyabama Institute of science and Technology School of Computing Department of Computer Science and Engineering Chennai600119 India Jeppiaar Institute of Technology Department of Electronics and Communication Engg. Tamilnadu Sriperumbudur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798331504403

The process of De Novo drug design involves the integration of artificial intelligence with computational intelligence techniques. The proposed system aims to provide precision therapeutics through the development of targeted chemical libraries designed for individual patient's needs. They also highlight the potential impact of precision medicine. The traditional approach involves trial and error methods in the combination of various chemicals for desired drug discovery that leads to the waste of time and energy. These challenges are overcome through the aid of artificial intelligence techniques. The interaction of molecules, physiochemical properties, and biological interaction of potential drugs are obtained through the aid of optimization algorithms. With the integration of computational intelligence, the design structure becomes more adaptable. This helps in the formation of chemically diverse compound libraries. The important aspect of the proposed system involves the ability of advanced precision therapeutics. The clinical information is necessary for the identification of molecular targets achieved through the aid of optimization algorithms. These obtained targets are necessary for the development of new drugs with high affinity and specificity. This helps in minimizing the off-target effects which improves the therapeutic efficacy. The novel molecular structure is generated using the Generative Adversarial Networks (GAN). The compound properties are optimized using reinforcement and an iterative learning process. The simpler structure of drug-like molecules is obtained through an evolutionary algorithm. Thus the proposed system provides rapid exploration of chemical space that leads to the formation of chemical compounds with personalized pharmacological profiles. © 2024 IEEE.

关键词： Drug discovery

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Group-based Slice Authentication for IoTs

Group-based Slice Authentication for IoTs

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2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023

作者： Liu, Xuewen Maimaiti, Saidiwaerdi Zhang, Kaisa Zuo, Peiliang Liu, Boya Zhang, Weijie Chuai, Gang Beijing Electronic Science and Technology Institute Department of Electronic and Communication Engineering China School of Physics and Electrical Engineering Kashi University India School of Electronic Engineering Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798350358971

Network slicing provides differentiated customization services for different users through virtualization technology. Currently, the 5G authentication and key agreement mechanism (5G-AKA) based on user unique identity identification and symmetric encryption algorithm cannot meet the practical needs of the '5G Internet of Things Plus' in terms of identity verification and access control for a large number of low-power and limited intelligent edge terminals, such as Internet of Things (IoTs) devices being unable to run complex security protocols, a large number of device slicing connections and handover requests leading to core network signaling congestion. In this paper, we investigate group-based authentication and handover mechanisms for multi-user and multi-service network slicing. The proposed solution can effectively ensure communication security, significantly reduce core network signaling interactions, reduce costs and increase efficiency for resource limited terminals, and promote energy conservation and carbon reduction in mobile communication. © 2023 IEEE.

关键词： 5G-AKA Group-based Mechanism IoTs Slice Authentication

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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Deep Learning Solutions for Enhanced Detection for Skin Lesions 2

Deep Learning Solutions for Enhanced Detection for Skin Lesi...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Shashikala, S.V. Anil Kumar, K.T. Chaithra, K.N. Bgs Institute of Technology Adichunchanagiri University Department of Computer Science & Engineering BG Nagara Mandya India Nitte Meenakshi Institute of Technology Department of Electronics & Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350372892

Due of the disease's quick spread, the recent spike in monkey pox outbreaks in more than 40 nations has caused serious public health concerns. Monkey pox can be difficult to diagnose in the early stages because of its similar clinical appearance to measles and chickenpox. When confirmatory Polymerase Chain Reaction (PCR) assays are not available, computer-aided lesion detection for monkey pox could play a major role in surveillance and early case discovery. Although deep learning techniques have demonstrated potential in automating the identification of skin lesions, a challenge is the inadequacy of training data that is especially related to monkey pox. Our research fills this vacuum by developing a deep learning model that can anticipate a range of skin conditions, such as tomato pox, chickenpox, smallpox, and monkey pox. Using mostly photos from publicly accessible websites and platforms, we use data augmentation methods to increase sample size. Moreover, we categorize these illnesses using pre-trained deep learning models like VGG-16, ResNet50, and InceptionV3, supplemented with an ensemble method for increased accuracy. We create a prototype graphical user interface (GUI) screening tool to ensure user-friendliness in clinical settings and to improve simplicity of usage. On an average the models have obtained 97% accuracy rate. © 2024 IEEE.

关键词： Graphical user interfaces

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Deep Learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. This paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). The employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. The research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. The model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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