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sHAP-Guided GWO and AbC Feature selection for Early Diabetes Prediction with XGboost 2

SHAP-Guided GWO and ABC Feature Selection for Early Diabetes...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pavan Kumar, v.s. Koteswara Rao Yarlagadda, s. Pilli, Neelima Koti Mani Kumar Tirumanadham, N.s. Muniyandy, Elangovan Ruth, Gulli Hyderabad India Sir C R Reddy College of Engineering Department of IT Eluru India S R K R Engineering College Department of CSE Bhimavaram India Sir C R Reddy College of Engineering Department of CSE Eluru India Saveetha Institute of Medical and Technical Sciences Department of Biosciences Chennai India B V Raju Institute of Technology Department of Computer Science and Business Systems Medak India

ISBN: (纸本)9798350371314

The study examines the relationship of autonomous motivation with physical activity in type 2 diabetes mellitus (T2DM) patients after participation in a fitness intervention trial. The study also longitudinally explores the various factors regarding motivation in the newly diagnosed population, which gives useful insight into dietary self-care and treatment of diabetes. This pointed towards research gaps that are existing and hence the further need of conducting research for gender-specific inequities, management of cardio metabolic risk, provision of autonomy support, psychological issues, and foot health among diabetic patients. This work hereby brings in feature selection with interpretable modelling techniques in diabetes prediction using optimization algorithms such as Grey Wolf Optimization (GWO) and Artificial bee Colony (AbC), together with sHAP-XGboost. Concerning the comparison between GWO and sHAP-XGboost and AbC with sHAP-XGboost, it can be said that the latter is the best performer. Concerning the comparison of GWO and sHAP-XGboost and AbC with sHAP-XGboost, the latter is said to exhibit the best with 82.26% of accuracy. The outcomes of this research can be very useful for improvement in diabetic therapies, and in particular, for tailored treatment options and overall outcomes for the patients. © 2024 IEEE.

关键词： Optimization

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A Fingerprint based Novel Cancelable biometric Authentication Technique 3

A Fingerprint Based Novel Cancelable Biometric Authenticatio...

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3rd IEEE Asian Conference on Innovation in Technology, AsIANCON 2023

作者： Aruna Ravi, K.R. Karthik, s. vineeth, b.v. Yogesh, T. sooda, Kavitha Karunakara Rai, b. B.M.S. College of Engineerig Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Electronics and Communication Engg Bangalore India

ISBN: (纸本)9798350302288

In recent years, the usage of diverse biometric technologies for reliable automatic human recognition has grown at an exponential rate. There is rising concern that, with the widespread use of biometric technologies, people's privacy and anonymity may be in risk. In contrast to passwords and credit cards, biometrics are linked to a person forever and cannot be altered if they are stolen. biometric patterns should be altered using non-invertible and revocable transformations to create cancelable biometric templates in order to avoid theft. A summary of latest advancement in different cancelable biometric systems for biometric template protection is provided. The shortcomings and benefits of current cancelable biometric systems are explored and point out promising research areas for development in this rapidly evolving domain. Also present a model of generating cancelable fingerprint template using Reed solomon error corrections, which can be used to authenticate a distorted biometric image. This approach provides an accuracy of 95%. © 2023 IEEE.

关键词： biometrics

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Comprehensive Analysis of Deep Learning Methods for COvID-19 Detection and Classification

Comprehensive Analysis of Deep Learning Methods for COVID-19...

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IEEE International Conference on engineering Education: Innovative Practices and Future Trends (AICERA)

作者： bindhu Prabha s v Divya P venkadesh Dept. of Computer Applications Noorul Islam Centre For Higher Education Kumarakovil Kanyakumari Tamil Nadu Dept.of CSE V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu Dept. of AI & DS V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu

The progress of precise recognition techniques and the evaluation of genetic connections are required because of the worldwide spread of COvID-19. As conventional techniques for COvID-19 detection are time-consuming and require accurate variables in order to minimize or cut short errors that could possibly occur, the COvID-19 epidemic's necessity for early diagnosis and surveillance led to the deployment of Deep Learning (DL) technology for efficient evaluation. Deep learning is a branch of artificial intelligence (AI) and Machine learning (ML), and due to its remarkable ability to assimilate new information, DL technology has quickly risen to prominence. Many DL architectures, like RNN, CNN, and long-term memory, can be utilized for forecasting, perdition, and classification of COvID-19. The intention of this paper is to conduct a comprehensive analysis of various deep learning methods, for detecting and classifying the COvID-19 virus using diverse data sources, such as chest X-rays, CT scans, and genomic sequences, to highlight their exceptional accuracy in identification. We also explore DL's involvement in analyzing the virus's genetic code, providing insight into how it evolved. It aims to deliver a summary of earlier research and its COvID-19 applications.

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Image based security system in cloud resource scheduling 5

Image Based Security System in cloud resource Scheduling

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Chakraborty, Koushik Dora Pravina, C.T. Arya, Nancy sharma, sunil Rakesh, shanu Kuttan singh, Arvind Kunar Adamas University Kolkata India Department of Mathematics Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Avadi India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University Gurugram India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Chouksey Engineering College Bilaspur Chhattisgarh India Department of Computer Science and Engineering S.V.N.I.E.T Barabanki India

ISBN: (纸本)9798400709418

The popularity of cloud computing in scheduling work processes, particularly logical work processes, is growing. The cloud computing environment may encounter significant issues with execution time and cost during resource allocation, resulting in disruptions in customer support quality. Text Passwords are right now a totally utilized insistence part, however graphical passwords and biometrics are likewise utilized. However, we really want to improve both comfort and security in any system to make it extremely secure. Image-based security systems leverage visual information and image processing techniques to enhance security measures. These systems use images or video streams from cameras to monitor, detect, and respond to security threats. Most likely, the framework employs our algorithm to improve performance. It utilizes parts of both Graphical and Text secret phrase plans. Advanced image processing algorithms analyze captured images for various security-related purposes, such as object detection, facial recognition, and anomaly detection. In contrast to the most well-known scheduling algorithms, such as multi-objective scheduling (MOs) and cost-conscious scheduling, the various scheduling algorithms in this proposed hybrid algorithm mannequin are used to make cloud services secure and highly available to end users. Identifying and verifying individuals based on facial features. This is used for access control, identity verification, and tracking individuals of interest. Analyzing images to detect unauthorized persons or objects in restricted areas, triggering alerts or alarms. In this investigation, we attempt to demonstrate the execution of various algorithm systems for the most well-known three static booking calculations. All the calculations are working out the all-out execution cost and timing of various calculations. © 2023 ACM.

关键词： security systems

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Efficient Automated Detection system for Pneumonia-Affected X-Ray Images using Neural Networks 7

Efficient Automated Detection System for Pneumonia-Affected ...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Kalaiyarasi, M. venkata sireesha, N. saravanan, s. Naga Malleswara Rao, D.s. Karthi, s. Karunanithi, K. Saveetha School of Engineering Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences SIMATS Chennai India Institute of Aeronautical Engineering Dundigal Department of Information Technology Telangana Hyderabad India B v Raju Institute of Technology Department of EEE Telangana Narsapur India Gokaraju Rangaraju Institute of Engineering and Technology Telangana Hyderabad India V.S.B Engineering College Department of CSE Karur India R&D Institute of Science and Technology Department of EEE Vel Tech Rangarajan Dr. Sagunthala Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

COvID-19, caused by the sARs-Cov-2 virus, has impacted over 250 countries worldwide. Early recognition of COvID-19 infections is essential to effectively manage this global outbreak. While chest X-rays are widely available and cost-effective imaging techniques, their accurate interpretation necessitates the expertise of knowledgeable professionals in assessing the severity of infectious. Even though many COvID-19 pneumonia virus recognition techniques are available, each method has advantages and disadvantages. This paper proposes an efficient automated detection system for pneumonia-affected X-ray images utilising a bilateral filter and RF classifier. The proposed method presented in this study was trained and evaluated using a publicly available X-ray dataset specifically designed for classifying tertiary and normal cases. Furthermore, the proposed method demonstrated reliable accuracy of 94% and recall of 87% in binary classification tasks. © 2023 IEEE.

关键词： COvID-19

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Comparative Analysis on Prediction Model of Hate speech Detection based on social Media Data Using Artificial Intelligence

Comparative Analysis on Prediction Model of Hate Speech Dete...

引用

Power, Control and Computing Technologies (ICPC2T), Conference on

作者： Pravin D. Kaware Anjali b. Raut Department of Computer Science & Engineering H.V.P.M’s College of Engineering & Technology Amravati India

ISBN: (数字)9798331530402

ISBN: (纸本)9798331530419

The exponential expansion of social media networking platforms has magnified the dissemination of hate speech, thereby requiring strong recognition systems to minimize its influence. The present work offers a comparative examination of many prediction models for the recognition of hate speech using social media data, employing Artificial Intelligence (AI) methodologies. The paper intends to assess the efficiency of several Machine Learning (ML) and Deep Learning (DL) algorithms for accurately detecting hate speech. The various strategies are evaluated using known datasets and measures including accuracy, recall, precision, and F1-score. In addition, useful insights on the most effective methods for identifying hate speech are presented. The findings suggest that deep learning approaches are exceptionally effective at catching the contextual nuances of hate speech. AI-based hate speech detection apps improve accuracy and efficiency. such systems are critical for creating a safer and more inclusive online.

关键词： Deep learning Analytical models Accuracy social networking (online) Computational modeling Hate speech speech recognition Predictive models Data models Context modeling

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Ensemble Approaches for Offensive Language Detection in Kannada

Ensemble Approaches for Offensive Language Detection in Kann...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Monish M N Kavitha sooda Dept. of Computer Science & Engineering B.M.S. College of Engineering Bengaluru India

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble’s performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs.

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Emergency Response Optimization system with Dynamic Traffic signal Control: (Life saver)

Emergency Response Optimization System with Dynamic Traffic ...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： Rajeshwari b.s vinay Y venu Gopal v Tauksik Anil Kumar viraj Chowdarapu Dept. of Computer Science Engineering B.M.S College of Engineering Bangalore India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

The traditional emergency response systems often face significant problems that can delay their services. These systems usually struggle with heavy traffic in cities, outdated ways to communicate, and old-fashioned methods for finding the best routes. These issues can be serious, especially in emergencies where every second matters. Also, the inability of these systems to adapt quickly to changing situations makes it even harder for emergency vehicles to get to where they need to be on time. To overcome these challenges, new technologies are being developed to improve how emergency responses are managed. One of these innovations is the Emergency Response Optimization system with Dynamic Traffic signal Control. This system uses advanced tracking technologies to provide ambulance tracking for patients and dynamic traffic signal control. This paper looks at the problems with traditional emergency services and how new technologies can make a big difference, especially focusing on the advantages of dynamic traffic control systems.

关键词： Technological innovation Web services Urban areas Transportation Transforms Traffic control Emergency services Real-time systems vehicle dynamics Optimization

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smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

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1st International Conference on software, systems and Information Technology, ssITCON 2024

作者： Gore, K.b. Chidambararaj, N. Kartheesan, L. Ghamande, Manasi vyankatesh sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. by implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RbM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DbN and KNN. © 2024 IEEE.

关键词： Irrigation

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Deep Learning Techniques For signal Processing On spherical Manifolds In Near-Field Applications 13

Deep Learning Techniques For Signal Processing On Spherical ...

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13th IEEE International Conference on Communication systems and Network Technologies, CsNT 2024

作者： Mohseni, Milad Kapula, Prabhakara Rao Dutt, Amit Pravallika, b. Rajalakshmi, b. Geetha, b.T. Islamic Azad University Department of Computer and Engineering Tabriz Iran B v Raju Institute of Technology Department of Ece Telangana State Narsapur India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Science Bangalore India Saveetha School of Engineering Simats Department of Ece Tamil Nadu India

ISBN: (纸本)9798350305463

Improvements in deep learning have benefited signal processing on spherical surfaces in particular. This work addresses deep learning for close-range spherical surface signal processing. Traditional signal processing algorithms may struggle to handle complex data on spherical surfaces. Deep learning has the potential to close this gap. Our deep learning in signal processing examination begins with its principles, difficulties, and accomplishments. The article goes on to illustrate how spherical geometry influences data encoding and signal processing, underscoring the need for understanding signal processing on spherical manifolds. The research emphasises the need for cutting-edge solutions to tackle non-Euclidean data representation challenges quickly. We are experimenting with several deep learning architectures for spherical manifold data. Among these architectures are attention mechanisms, GNNs, CNNs, and so on. Integrating these models with spherical data layers and methodologies should allow for a thorough evaluation of their ability to detect complex close-range *** learning algorithms increase near-field signal processing on spherical manifolds in terms of accuracy, efficiency, and resilience, according to the results. Deep learning architectures for curved spherical data processing are advanced in this work. This objective necessitates research on its theoretical underpinnings, existing approaches, and practical applications. These unique and adaptable data management solutions provide remarkable data management on spherical manifolds for near-field applications. Their data management systems are more precise and trustworthy than earlier ones. © 2024 IEEE.

关键词： Network architecture

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