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DCVCP: Experimental Evaluation of Leukaemia Disease Prediction Using Deep Convoluted Vector Classification Principle

DCVCP: Experimental Evaluation of Leukaemia Disease Predicti...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Balachander, Bhuvaneswari Sivamurugan, S. Gomathi, M. Jayanthi, J. Adkane, R.V. Kanan, M. Dr.M.G.R Educational and Research Institute Department of Electronics and Communication Engineering Chennai India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India Dhanalakshmi College of Engineering Department of Cyber Security Chennai India Sona College of Technology Department of Computer Science and Engineering Salem India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Engineering University of Business and Technology Department of Industrial Engineering Jeddah21448 Saudi Arabia College of Engineering Zarqa University Department of Mechanical Engineering Zarqa Jordan

ISBN: (纸本)9798331543617

A wide variety of cancers, including skin, breast, lung, and blood malignancies such as lymphoma and leukemia, are defined by the uncontrolled growth of cells within the body. Acquired lymphoblastic leukemia is among the most prominent cancers. Blood cancer diagnoses are notoriously time-consuming because hematologists often make mistakes. With the use of cutting-edge tools like Deep Learning and Machine Learning, this study considers a fresh approach to leukemia classification. The precise diagnosis of blood cancer is a crucial medical process, but it is also one of the most costly and challenging for clinical staff to do. Blood cancer is one of many severe forms of the disease. The devastating and deadly blood cancers, lymphoma and leukemia, impact people of all ages and genders, and they are a leading cause of other illnesses and an elevated survival rate. Damage to and an increase in immature monocytes, neutrophils, eosinophils, and lymphocytes are hallmarks of both leukemia and lymphoma. Predicting and treating blood cancer early is crucial for improving survival rates in the health industry. These days, a number of manual methods exist for analyzing and predicting blood cancer based on microscopic medical reports of white blood cell images;this method is both reliable and deadly. It takes a lot of time and effort to manually forecast and analyze eosinophils, lymphocytes, monocytes, and neutrophils. The Deep Convoluted Vector Classification Principle (DCVCP) is a new deep learning technique for leukemia illness detection that was described in this study. To test how well it works, it was cross-validated with the current method, the Convolutional Neural Network (CNN). findings from this study show that DCVCP can effectively leverage data from network sensors to identify blood cancer, which might lead to faster findings and lower costs in clinical settings. © 2024 IEEE.

关键词： Lung cancer

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Facial Expression Recognition: Detection and Tracking 13th

Facial Expression Recognition: Detection and Tracking

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13th International Advanced Computing Conference, IACC 2023

作者： Bhatia, Abhay Kumar, Manish Kumar, Jaideep Kumar, Anil Verma, Prashant Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee India Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India Department of IoT Raj Kumar Goel Institute of Technology Ghaziabad India Department of of Information Technology Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9783031566998

One of the simplest ways to tell someone else apart from you is by their face. A personal identification system like face recognition may use an individual’s traits to identify them. Detection of any Face and Stage are the two stages of process of the face recognition of human, which is used for facial image recognition modal (face recognition) in biometric technology. The Eigen face method and the Fisher face method are the two categories of methods that are frequently used in created facial recognition patterns. Principal Component Analysis (PCA) for countenance is used to reduce the number of faces in three-dimensional space by the Eigen face approach for image facial recognition. Finding the eigenvector that resembled the most crucial Eigen value of the face image was the major goal of applying PCA [1] on face recognition using Eigen faces [2]. Image processing is used in face detection systems with face recognition. This requires mat lab software, which is the required program. Neural networks are categorized as deep learning. Deep learning’s foundational component, feature learning, aims to obtain hierarchical information using hierarchical networks in order to address significant issues that previously required artificial design features. The framework used is termed as Deep Learning and it may include n number of significant algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Face recognition

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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking

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computers, Materials & Continua 2025年第5期83卷 3135-3163页

作者： Shu-Chuan Chu Libin Fu Jeng-Shyang Pan Xingsi Xue Min Liu School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210000China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266000China Department of Information Management Chaoyang University of TechnologyTaichung40601TaiwanChina School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resource Nanjing University of Information Science and TechnologyNanjing210000China

Evolutionary algorithms have been extensively utilized in practical ***,manually designed population updating formulas are inherently prone to the subjective influence of the *** programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world *** paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human *** modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the *** designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update *** Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the *** validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark ***,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking *** experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency.

关键词： Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework

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DEVELOPMENT OF AI-BASED POSTURE MONITORING SYSTEM TO ASSIST YOGA TRAINING

DEVELOPMENT OF AI-BASED POSTURE MONITORING SYSTEM TO ASSIST ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Anitha Elavarasi, S. Ankit Kumar, P. Jayanthi, J. Department of Computer Science and Engineering Sona College of Technology Salem India

ISBN: (纸本)9798350305708

In the modern era of remote fitness and wellness, maintaining correct posture during exercises and yoga practices is vital for maximizing the benefits and preventing injuries. However, monitoring one's posture can be challenging, often leading to improper form and reduced effectiveness. To address this issue, this project presents an innovative real-time posture monitoring system using computer vision and pose estimation techniques. The system employs the Mediapipe library to detect and analyze the user's body posture through a camera feed. It begins by capturing a reference posture from an image and calculating its landmark coordinates. These coordinates serve as the baseline for correct posture. As the user performs exercises in front of the camera, their live posture is continuously evaluated against the reference posture using Euclidean distance as a similarity metric. A crucial contribution of this project lies in its ability to provide immediate feedback to the user regarding their posture. The system visualizes landmarks on the user's body, highlighting correct and incorrect positions in distinct colors. If the similarity score between the live posture and the reference posture surpasses a certain threshold, the landmarks appear in green, indicating good posture. Conversely, red landmarks indicate discrepancies, prompting the user to adjust their form. Experimental results showcase the effectiveness of the proposed system in real-time posture assessment. Users can actively observe their posture and make necessary adjustments, fostering improved exercise outcomes and reduced risk of injuries. By offering a convenient and immediate feedback mechanism, this project bridges the gap between remote fitness practices and professional guidance, ultimately promoting safer and more effective workout routines. © 2023 IEEE.

关键词： Artificial Intelligence Deep learning Mediapipe online Yoga trainer

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Performance Analysis of Machine Learning Techniques for Detecting Money Laundering in Bitcoin Transactions

Performance Analysis of Machine Learning Techniques for Dete...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Salahi, Parisa Shady, Reem Doush, Iyad Abu Kandil, Marwa College of Engineering and Applied Science American University of Kuwait Kuwait Kuwait Computer Science Department Yarmouk University Irbid Jordan

ISBN: (纸本)9798331533137

More people are relying on Cryptocurrencies, yet these currencies are not controlled by any central authority or governments. Bitcoin transactions’ anonymity facilitates money laundry activities by cyber-criminals. Traditional Anti-Money Laundering solutions, often rule-based, suffer from high false positive rates, leading to substantial operational costs. Machine Learning (ML) algorithms offers efficient analysis and identification of abnormal patterns in vast amounts of data;thus predicts suspicious transactions. This paper evaluates the effectiveness of four different ML algorithms in classifying Bitcoin transactions as either licit, or illicit transactions. The ML models were trained on the Elliptic dataset of Bitcoin transactions categorized into real entities belonging to licit and illicit categories [1]. The models were evaluated by the confusion matrix, accuracy, and features importance study. The experimental results proved that Random Forest (RF) and Extreme Gradient Booster (XGBoost) models achieved the highest accuracy of 95.1% and 95.6%, respectively. This indicates that RF and XGBoost models effectively managed the data’s complexity and accurately predicted suspicious transactions. ©2024 IEEE.

关键词： Laundering

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Gesture Recognition using Convolutional Neural Network 2

Gesture Recognition using Convolutional Neural Network

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2nd International Conference on Advances in Computational Intelligence and Communication, ICACIC 2023

作者： Shanmuga Priya, G. Nitisha Sree, V. Magisha, K. Pooviga, S. Sri Krishna College of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350318456

Hand gesture detection, which is adaptable and user-friendly, is one of the most active study areas in the field of human-computer interfaces. Using the gesture recognition system, a system is designed that can be deployed to communicate with disabled people or to administer a product. Excellent internal consistency commonalities in hand gesture postures, slight variances, non-uniform surroundings, with user-specific hand form and shape variations all offer considerable obstacles to creating an efficient hand gesture detection mechanism. In this article a hand gesture detection method which is user independent is examined using custom characteristics such convolutional neural networks (CNN). A technology known as Gesture Recognition uses mathematical algorithms to interpret human gestures. The dataset is collected and stored in the form of files and folders. Using CNN algorithm, the collected dataset is compared with the threshold value of the given input and thus the result is presented in the form of text or voice. Gesture Recognition System is an inventive user interface that eliminates the challenges associated with using remote controls for home appliances by replacing the remote-control system with hand gestures. Deep learning and computer vision are two tools that can be used to further the cause. Using the suggested method, an authentic implementation of gesture - based system is constructed and evaluated. © 2023 IEEE.

关键词： Remote control

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Integration of IoT and AI for Enhanced Efficiency and Control in Smart Energy Management Systems 2

Integration of IoT and AI for Enhanced Efficiency and Contro...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Naganandhini, S. Saravanan, S. Sammeta, Naresh Ramkumar, S. PSNA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Dindigul India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Andhra Pradesh Near Vijayawada India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798331534950

This paper presents a detailed analysis of how the integration of Internet of Things (IoT) and artificial intelligence (AI) to energy management systems (EMS) has a possibility of enhancing energy efficiency and control across various sectors. Therefore, this paper seeks to analyze the new developments in smart EMS through analyzing the possibilities of the combination of IoT and AI. Hence, the research utilises the hybrid approach with regard to the applicable methodology that includes numerical and analytical description, qualitative case and example analysis, and the actual implementation. Sophisticated quantitative analysis was also performed to analyze how the sensors of IoTs and the AI algorithm would affect the real-time energy monitoring, the demand forecasting, and the automated energy control systems. This paper presented case studies which offered more details on implementation including the processes, operations and such gains as experienced from employing IoT and AI. It is evident from the analysis that IoT enables detailed data acquisition and real-time observation and monitoring of devices, whereas AI optimises predictive modelling and decision-making. These result to imperative upgrades in energy efficiency, cuts in operation costs and strengthening in system performance. This paper provides an insight into the subject of smart EMS' application of IoT and AI in the context of energy management and presents recommendations to the practitioners and scholars. The implications also brought out future research directions in terms of improving the AI models and extending the use of IoT across a range of energy management scenarios. © 2024 IEEE.

关键词： Demand Forecasting Energy Efficiency Internet of Things Predictive Analytics Smart Energy Management Systems

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Deep Learning-Based Approach for Calorie Estimation in Indian Foods 2

Deep Learning-Based Approach for Calorie Estimation in India...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Chauhan, Naman Revathy, S. Murali, P. Basker, N. Saranya, S. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engg. Chennai Ramapuram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engg. Chennai India Sona College of Technology Department of Computer Science and Engg. Salem India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engg Vellore Institue of Technology Chennai Campus India

ISBN: (纸本)9798350373110

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work investigates the process of estimating the calories present in the food images. This research work was carried out in three phases. The first phase involves the categorization of food images, such as boiled, fried in oil, or junk. This research work introduces a novel deep neural model by incorporating the MobileNet-V2 framework. In the second phase, the quantity of the labelled food is estimated. Further, it involves computing the calories present in the food using the Google search API. The details of the ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used to evaluate the workings of the proposed model. This research work has secured plausible results with an overall accuracy of 95.21%, comparatively better than the existing approaches. © 2024 IEEE.

关键词： Calorie Estimation Deep Learning Google Search API Image Classification MobileNet-V2 Neural Network

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Deepfake Detection System 2

Deepfake Detection System

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Shrivas, Shaswat Rai, Aditya Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

Detecting deepfake content presents a formidable challenge, necessitating advanced methodologies. This paper proposes a holistic strategy employing Facenet-pytorch, MTCNN, and InceptionResnetV1 for robust deepfake detection. Facenet-pytorch serves as the cornerstone for facial feature recognition, while MTCNN efficiently identifies faces in images during preprocessing. InceptionResnetV1 scrutinizes visual details to detect subtle anomalies indicative of manipulation, such as unnatural facial expressions and incongruent lip synchronization. Our approach underscores the importance of maintaining a delicate balance between accurate detection and individual privacy rights. By leveraging these advanced models, we achieve significant strides in differentiating manipulated content from authentic media, contributing to the ethical deployment of deepfake detection technologies. © 2024 IEEE.

关键词： Biometrics

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Leveraging XGBoost based GBM for Proactive Detection of Man-in-the-Middle Cyber Attacks

Leveraging XGBoost based GBM for Proactive Detection of Man-...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Manjaragi, Shivanand V. Asrani, Deepak Kumar, Arvind Sandesh, R. Maindola, Meenakshi Sai, Aarush Department of Computer Science and Engineering Hirasugar Institute of Technology Karnataka Nidasoshi591236 India Department of Computer Science and Engineering MIET Uttar Pradesh Greater Noida India SCA MRIIRS Haryana Faridabad India Department of Computer Science and Engineering ATME College of Engineering Karnataka Mysuru India Department of Computer Science Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Department of CSBS Gyan Ganga Institute of Technology and Sciences Madhya Pradesh Jabalpur India

ISBN: (纸本)9798350374933

Man-in-the-Middle (MitM) cyber-attacks provide a substantial risk to network security, since they allow hostile individuals to intercept and alter communications between two parties. Conventional detection approaches frequently fail to recognize these complex threats, requiring the creation of more resilient and precise solutions. This study introduces a model called XGBoost-based Gradient Boosting Machine (GBM) to identify MitM attacks in advance. The system employs extensive datasets from Kaggle, namely CICIDS 2017 and UNSW-NB15, for the purpose of training and evaluating the model. By carefully preparing the data, selecting relevant features, and doing thorough cross-validation with hyperparameter optimization, the suggested model attains exceptional performance metrics. The assessment findings indicate an accuracy of 0.98, precision of 0.97, recall of 0.96, F1-Score of 0.965, and a remarkable ROC-AUC score of 0.99. Our model regularly outperforms existing technologies like as Random Forest and Traditional GBM in identifying MitM attacks, achieving higher performance metrics across the board. This research demonstrates the effectiveness of the XGBoost-based GBM model in improving cybersecurity defenses and suggests that it might serve as a standard for future advancements in cyber-attack detection systems. The suggested system's exceptional accuracy and resilience make it an invaluable tool for proactively detecting and reducing MitM attacks. © 2024 IEEE.

关键词： Cyber attacks

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