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Breast Cancer Prediction Using Artificial Intelligence technology 4

Breast Cancer Prediction Using Artificial Intelligence Techn...

4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Akila, V. Christaline, J. Anita Srm Institute of Science and Technology Vadapalani Dept. of Electronics and Communication Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

The aim of the project is to compare the performance of four different machine learning algorithms for breast cancer prediction such as decision tree, logistic regression, XG boost, and CAT boost. We used a dataset of patient medical records containing various clinical factors to train and test the algorithms. Accurate diagnosis and early detection are essential for enhancing patient outcomes. Data mining is a prominent tool in the healthcare industry for processing massive amounts of data. To examine massive amounts of complicated medical data, researchers use a variety of data mining and machine learning approaches. The use of these strategies can help medical practitioners forecast the onset of breast cancer. © 2024 IEEE.

关键词： Algorithms Breast cancer Data mining Machine Learning

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Development of a dual sensor QCM array based electronic nose for detection of citronellal and geraniol in citronella essential oil

Development of a dual sensor QCM array based electronic nose...

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2024 IEEE Calcutta Conference, CALCON 2024

作者： Gangopadhyay, Deepam Ankit Naskar, Jeet Kundu, Sumit Nag, Shreya Banerjee, Mahuya Bhattacharyya Roy, Runu Banerjee Jadavpur University Dept of Instrumentation and Electronics Engineering Kolkata India University of Engineering and Management Dept of Electronics and Communication.Engineering Kolkata India Govt College of Engineering and Textile Technology Dept of Textile Technology Serampore India Govt College of Engineering and Textile Technology Dept of Computer Science Engineering Serampore India

ISBN: (纸本)9798331516666

Citronella essential oil (CEO) has multitudes of industrial applications like insect repellents, aroma therapeutic substances, personal care products, food preservatives, etc. due to the presence of citronellal and geraniol as the two vital volatile organic compounds (VOC). Herein, a low-cost electronic nose (E-Nose) setup has been developed employing a Quartz Crystal Microbalance (QCM) based dual sensor array system for effective simultaneous detection of citronellal and geraniol in the CEO sample. The sensors have been prepared using molecular imprinted polymer (MIP) technology for better sensitivity and selectivity. Principal component analysis was used to assess the performance of this multi-sensor array setup. The system could detect and discriminate both citronellal and geraniol simultaneously from the same CEO sample with a high separability index of 38.996. © 2024 IEEE.

关键词： Quartz crystal microbalances

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT Sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic Systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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Indian Sign Language Interpretation using Convolutional Neural Networks 10

Indian Sign Language Interpretation using Convolutional Neur...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Agarwal, Agrima Kumar, Vishal Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9781665490993

Sign language is the most common means of communication among the speech- and hearing-impaired. Just like all other languages, tools are being developed for interlanguage translation from sign language to text;however, the complexity of the sign language makes it difficult to create a computer vision model. The focus of this study is to tackle the problem with a vision-based approach using deep learning. In this work, an 8-layer Convolutional Neural Network (CNN) aimed at sign language recognition has been proposed. The model extracts feature from input images using convolutional filters before passing them to dense layers. A validation accuracy of 99.34% has been achieved on a self-created dataset comprising of 52 classes. The model has also been tested on a publicly available dataset to evaluate its efficacy. © 2023 IEEE.

关键词： computer vision

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm

Smart Grid Solar Tracking Optimization using Deep Reinforcem...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Kavitha, H.S. Anu, H. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Sharmila, N. Naveen, K.B. Sheela, S. Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Rns Institute of Technology Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagar India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of solar power into smart grids necessitates efficient solar tracking to maximize energy capture. Conventional methods like Single-Axis Trackers (SAT) and Dual-Axis Trackers (DAT) have limitations, including high maintenance costs, limited adaptability, and inefficiency under variable weather conditions. These drawbacks result in suboptimal energy generation, hindering the performance of smart grids. The proposed Smart Grid Solar Tracking Optimization using Deep Reinforcement Learning Algorithm (SGST-DRLA) addresses these issues by utilizing a Deep Reinforcement Learning (DRL) approach to dynamically optimize solar panel orientation. Unlike static control strategies of SAT and DAT, SGST-DRLA continuously learns from realtime data, adjusting the tracker's position to maximize sunlight exposure and energy capture. This adaptive method enhances tracking precision and reduces operational costs. SGST-DRLA significantly improves performance metrics, achieving a 0.25% increase in energy capture efficiency, a 0.20% reduction in operational costs, and a 0.30% improvement in adaptability to varying weather conditions compared to traditional methods. This approach not only optimizes solar tracking in smart grids but also supports sustainable and efficient renewable energy integration, making it a viable solution for overcoming the limitations of existing tracking systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Comparative Analysis of Convolutional Neural Network Models for Early Detection of Corn Leaf Diseases: Inception vs. MobileNet 2

Comparative Analysis of Convolutional Neural Network Models ...

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

作者： Sanjana, N. Immanual, R. Sheela Sobana Rani, K. Sri Ramakrishna Institute of Technology Computer Science and Engineering Coimbatore India Sri Ramakrishna Institute of Technology Mechanical Engineering Coimbatore India Karpagam College of Engineering Electronics and Communication Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Corn is one of the major crops in India. In agriculture, early diagnosis of corn leaf disease is essential for increasing the yield of crops. One of the most widely grown grains worldwide is maize. Several leaf diseases, including corn common rust, corn grey leaf spot, and corn leaf blight, are prevalent and can cause significant damage to corn harvests. In their early stages, the symptoms of many leaf diseases are hardly distinguishable. This proposed work is a comparative analysis of two prominent convolutional neural network models, Inception version 3 and MobileNet version 2, for the corn leaf disease detection dataset. The research seeks to illuminate their respective performance and computational characteristics by employing the same dataset and consistent settings, including hyper parameters, data augmentation methods, and simulation parameters. MobileNet emerges as a frontrunner in training accuracy, boasting an impressive 95 percent, compared to Inception's 89 percent0. This study substantially contributes to agricultural disease detection by offering empirical insights and a foundation for enhanced model development and real-world applications. © 2024 IEEE.

关键词： Plant diseases

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Reliability Assessment of a New General Matching Composed Network

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China Communications 2024年第2期21卷 245-257页

作者： Zhengyuan Liang Junbin Liang Guoxuan Zhong Guangxi Key Laboratory of Multimedia Communications and Network Technology School of ComputerElectronics and InformationGuangxi UniversityNanning 530004China Jiangxi Engineering Laboratory of IoT Technologies for Crop Growth Ji’an 343000China

The reliability of a network is an important indicator for maintaining communication and ensuring its stable operation. Therefore, the assessment of reliability in underlying interconnection networks has become an increasingly important research issue. However, at present, the reliability assessment of many interconnected networks is not yet accurate,which inevitably weakens their fault tolerance and diagnostic capabilities. To improve network reliability,researchers have proposed various methods and strategies for precise assessment. This paper introduces a novel family of interconnection networks called general matching composed networks(gMCNs), which is based on the common characteristics of network topology structure. After analyzing the topological properties of gMCNs, we establish a relationship between super connectivity and conditional diagnosability of gMCNs. Furthermore, we assess the reliability of g MCNs, and determine the conditional diagnosability of many interconnection networks.

关键词： conditional diagnosability interconnection networks network reliability super connectivity

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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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Metaheuristic Optimization Algorithm for Signals Classification of Electroencephalography Channels

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computers, Materials & Continua 2022年第6期71卷 4627-4641页

作者： Marwa M.Eid Fawaz Alassery Abdelhameed Ibrahim Mohamed Saber Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Electronics and Communications Engineering Faculty of EngineeringDelta University for Science and TechnologyMansoura11152Egypt

Digital signal processing of electroencephalography(EEG)data is now widely utilized in various applications,including motor imagery classification,seizure detection and prediction,emotion classification,mental task classification,drug impact identification and sleep state *** the increasing number of recorded EEG channels,it has become clear that effective channel selection algorithms are required for various *** Whale Optimization Method(Guided WOA),a suggested feature selection algorithm based on Stochastic Fractal Search(SFS)technique,evaluates the chosen subset of *** may be used to select the optimum EEG channels for use in Brain-computer Interfaces(BCIs),the method for identifying essential and irrelevant characteristics in a dataset,and the complexity to be *** enables(SFS-Guided WOA)algorithm to choose the most appropriate EEG channels while assisting machine learning classification in its tasks and training the classifier with the ***(SFSGuided WOA)algorithm is superior in performance metrics,and statistical tests such as ANOVA and Wilcoxon rank-sum are used to demonstrate this.

关键词： Signals metaheuristics optimization feature selection multilayer perceptron support vector machines

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