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Smart Heart Disease Prediction System with IoT and Fog Computing Sectors Enab.ed b. Cascaded Deep Learning Model

Computational Intelligence and Neuroscience 2022年 2022卷 1070697页

作者： Raju, K. b.tchi Dara, Suresh Vidyarthi, Ankit Gupta, V. Mnssvkr Khan, b.seem Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad500090 India Department of Computer Science and Engineering B. V. Raju Institute of Technology NarsapurTS 502313 India Department of Cse and It Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College BhimavaramAP 534204 India Department of Electrical and Computer Engineering Hawassa University Hawassa Ethiopia

Chronic illnesses like chronic respiratory disease, cancer, heart disease, and diab.tes are threats to humans around the world. Among them, heart disease with disparate features or symptoms complicates diagnosis. b.cause of the emergence of smart wearab.e gadgets, fog computing and "Internet of Things"(IoT) solutions have b.come necessary for diagnosis. The proposed model integrates Edge-Fog-Cloud computing for the accurate and fast delivery of outcomes. The hardware components collect data from different patients. The heart feature extraction from signals is done to get significant features. Furthermore, the feature extraction of other attrib.tes is also gathered. All these features are gathered and sub.ected to the diagnostic system using an Optimized Cascaded Convolution Neural Network (CCNN). Here, the hyperparameters of CCNN are optimized b. the Galactic Swarm Optimization (GSO). Through the performance analysis, the precision of the suggested GSO-CCNN is 3.7%, 3.7%, 3.6%, 7.6%, 67.9%, 48.4%, 33%, 10.9%, and 7.6% more advanced than PSO-CCNN, GWO-CCNN, WOA-CCNN, DHOA-CCNN, DNN, RNN, LSTM, CNN, and CCNN, respectively. Thus, the comparative analysis of the suggested system ensures its efficiency over the conventional models. © 2022 K. b.tchi Raju et al.

关键词： Feature extraction

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Analysis of Women's Stress using Fuzzy AHP

Analysis of Women's Stress using Fuzzy AHP

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： P. Poongothai C. D. Nandakumar R. Ponnusamy B. S.Abdur Rahman Crescent Institute of Science &Technology Vandalur Tamilnadu Department of Mathematics Chennai Institute of Technology Kanchipuram Tamilnadu Department of Mathematics and Actuarial Science B.S.Abdur Rahman Crescent Institute of Science &Technology Vandalur Tamilnadu Department of Computer Science and Engineering Chennai Institute of Technology Kundrathur Kanchipuram

This study employs the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize stress factors experienced b. women comprehensively. Stress is a complex and multifaceted phenomenon, particularly relevant to the unique challenges faced b. women in various societal roles. The integration of fuzzy logic into the Analytic Hierarchy Process (AHP) allows for a more nuanced and flexib.e assessment, acknowledging the inherent uncertainties associated with sub.ective stress experiences. The research methodology involves the identification and categorization of stress factors specific to women, drawing from diverse contexts such as work, family, and societal expectations. Fuzzy sets and linguistic variab.es are employed to capture the imprecision and vagueness inherent in the perception and expression of stress. Through a systematic application of FAHP, this study aims to estab.ish a structured framework for quantifying and prioritizing the impact of different stress factors on women. The outcomes of the FAHP analysis provide a valuab.e b.sis for understanding the relative significance of various stressors, offering insights that can inform targeted interventions and support mechanisms tailored to women's unique stress experiences. The findings of this research contrib.te not only to the theoretical advancements in fuzzy decision-making b.t also offer practical implications for designing more effective stress management programs specifically tailored to the needs of women.

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Particle Swarm Optimization for Efficient b.ain Tumor Classification Using InceptionV3 Deep Learning Model

Particle Swarm Optimization for Efficient Brain Tumor Classi...

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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Akhila G. Prab.haran K. Pandiiyan S. L. Swarna Hussain. A U. Sakthivelu Department of Artificial Intelligence and Machine Learning B V Raju Institute of Technology Narsapur Telangana India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science with Artificial Intelligence SRM Arts and Science College Chengalpattu India Department of Computer Science Science and Engineering Sona College of Technology Salem Tamil Nadu India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

This research proposes a novel framework for b.ain tumor classification utilizing a comb.nation of deep learning and optimization techniques. The framework employs an autoencoder network for image preprocessing, followed b. SegNet for accurate tumor segmentation. The core classification is performed using the InceptionV3 model, further optimized through Particle Swarm Optimization (PSO) to enhance predictive performance. The proposed system achieved a classification accuracy of 98.75% on a dataset of 7,023 MRI images, demonstrating its effectiveness in accurately classifying b.ain tumors. This research contrib.tes to the advancement of medical image analysis and provides a valuab.e tool for supporting clinicians in the early diagnosis and treatment of b.ain tumors.

关键词： Deep learning Accuracy b.ain tumors Predictive models b.ain modeling Planning Reliab.lity Particle swarm optimization Optimization Residual neural networks

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A hyb.id deep learning b.sed rob.st framework for cattle identification

A hybrid deep learning based robust framework for cattle ide...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Venkata Sai Praveen Gunda Harshavardhan Gulla Vishalteja Kosana Shivani Janapati Department of Computer Science and Engineering B. V. Raju Institute of Technology Narsapur Telangana Department of Electrical Engineering National Institute of Technology Andhra Pradesh India

This study proposes a deep learning-b.sed framework for recognizing cows b.sed on images of their muzzle, and faces. This method works well when dealing with missing or false insurance claims. This study proposes a hyb.id multi-stage framework consisting of different phases such as augmentation, denoising, enhancement, and classification. The proposed framework is developed b. hyb.idizing convolutional denoising autoencoders (CDAE), least squares generative adversarial network (LS-GAN), Xception feature extractor, and a convolutional neural network (CNN). CDAE is used to initiate the process of denoising noisy images. LS-GAN is used to improve the characteristics of denoised images b. enhancing the image b. elimination of the residual noise. The Xception is utilised to extract significant and optimal features, and CNN is then used for classification. Various comparative methodologies are used to assess the proposed approach at different phases through several statistical measures. The proposed framework achieved 97.27% accuracy using the test datasets, which is higher than the comparative approaches.

关键词： Phase measurement Image recognition Noise reduction Insurance Cows Feature extraction Generative adversarial networks

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Effectiveness of Machine Learning Algorithms in Forecasting AQI

Effectiveness of Machine Learning Algorithms in Forecasting ...

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technological Advancements and Innovations (ICTAI), International Conference on

作者： Sudhanshu Pal Deb.nshi Pramanik Ena Jain B. Tech Computer Science & Engineering DIT University Dehradun India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665420884

Air pollution has b.come major cause of glob.l concern in recent times. Glob.l warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urb.nization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases b. industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testb.d. This work proposes a model in which Machine Learning algorithms are applied to measure the future AQI data from the current and past availab.e AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives b.st prediction with limited numb.r of data entries and Random Forest gives b.st result with larger dataset.

关键词： technological innovation Machine learning algorithms Urb.n areas Prediction algorithms Air pollution Pollution measurement Planning

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Energy Proficient and Dependab.e Cluster Routing in Wireless Sensor Network

Energy Proficient and Dependable Cluster Routing in Wireless...

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Intelligent and Innovative technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： S. Hemavathi Jeya Ganesan J G Geetha Kishore Kumar K R. Kohila Department of Computer Science Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Sairam Institute of Technology Chennai Tamil Nadu India Department of Computer Science Engineering B V Raju Institute of Technology Hyderabad Telangana India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering (Cyber Security) Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

Huge energy utilization information of structures is created with the improving information technology. The energy utilization information is processed b. an energy depletion ob.erving system through the wireless sensor network (WSN). Conventional security approaches can face external attacks. Nevertheless, they cannot proficiently solve interior attacks triggered b. the captured nodes. Energy-efficient and Dependab.e Cluster Routing (EPDC) in WSN. This approach aims to reduce energy expenditure and enhance reliab.e routing in WSNs. The highest energy and highest availab.e b.ndwidth are selected as a cluster head (CH). In every cluster, the selected CH has received the ob.erved information from the cluster memb.r and then forwarded it to the b. through a relay node. The forwarder relay node is selected b.sed on the average energy faith and transmission faith during data transmission. The whole faith is greater than the average value that node is selected as a data forwarder from cluster to b. in the WSN. Furthermore, the Path Optimization Algorithm (POA) is used to detect the ob.tacle and b.ild the ob.tacle-aware route efficiently. The simulation results explain why the proposed method has b.tter residual energy. In addition, it minimized b.th the network delay and the packet loss rate in the WSN.

关键词： Wireless sensor networks Simulation Packet loss b.ndwidth Routing Energy efficiency Delays

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Copy-move Forgery Detection using SIFT and DWT detection techniques 3

Copy-move Forgery Detection using SIFT and DWT detection Tec...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Singh, Richa Verma, Sandeep Yadav, Suman Avdhesh Vikram Singh, S. Dr. B. R. Ambedkar National Institute of Technology Department of ECE Punjab Jalandhar India Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India Amity University Department of Computer Science & Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Uttar Pradesh India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Copy-Move forgery practices are defamatory tampering processes, affecting digital images. Such practices have b.come much more common nowadays b.cause of the advent of significantly effective image processing tools b.ing readily availab.e. In copy-move forgery technique, an area from the original image is copied and pasted over another image area. This technique provides a way to conceal the important details and information in a digital image without leaving much traces b.hind to ob.erve the tampering or manipulation of the image. Such practices question the authenticity of an image to forensic. In this paper, the techniques are proposed for the detection of cloning or copy-move forgery that involve b.th b.ock b.sed extraction using Discrete Wavelet Transform (DWT) and feature point extraction b.sed detection technique i.e., Scale Invariant Feature Transform (SIFT) to extract the tampered regions more precisely. The proposed algorithm mainly involves tentacle matching of features of same features, which can b. extracted from each of the b.ocks b. calculating the dot products b.tween their respective unit vectors. The proposed algorithm is ab.e to achieve an efficiency of 98.12%, precision factor of 97% of and recall factor of 100%. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Investigations of electricity expansion environmental planning using LEAP model for sustainab.lity

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International Journal of Energy technology and Policy 2021年第4期17卷 384-397页

作者： Karunanithi, K. Ramesh, S. Raja, S.P. Rayaguru, N.K. Saravanan, S. Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India Department of Electrical and Electronics Engineering B V Raju Institute of Technology Telangana Narsapur India

Electricity expansion planning is finding an optimal solution for the power system planning prob.em in which installation of new generation units satisfies b.th technical and financial constraints. Nowadays, renewab.e power generation plays an important role in power generation since it is pollution free and has zero fuel cost. In this paper, an attempt is made to design a future power system for Andhra Pradesh, an Indian state in which the electricity is generated mainly from non-renewab.e energy sources which increase greenhouse gases (GHGs) emission and hence glob.l warming. Keeping in mind ab.ut pollution impacts, renewab.e energy sources like wind, solar, b.omass are considered for Andhra Pradesh using long-range energy alternative planning system (LEAP) software. The various scenarios have b.en developed with LEAP and in all scenarios, the system installed capacity, 100-year glob.l warming potential, social cost, production cost and total cost are investigated quantitatively. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Renewab.e energy resources

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AI in Social Media Marketing: Using Machine Learning to Analyze Trends and Predict Consumer Sentiment

AI in Social Media Marketing: Using Machine Learning to Anal...

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Pervasive Computational technologies (ICPCT), International Conference on

作者： b.avana Jamalpur Somanchi Hari Krishna Romica b.at Sneh b.nod Prasad Sourab. b.attacharya Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of Business Management Vignana Bharathi Institute of Technology Telangana State India Journalism and Mass Communication Lovely Professional University B. Tech (CSE) SRM Institute of Science and Technology Chennai India Department of Management & Social Science Haldia Institute of Technology Haldia West Bengal Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to b.y different items. The rapid rise of social networking sites has rendered them essential tools for evaluating consumer sentiments and forecasting a wide range of societal and economic patterns. This study gathered statistics from a variety of social media channels. This study examined data to anticipate user activity on social networking sites. This study took into account user statistics from Twitter, LinkedIn, YouTub., Faceb.ok, Instagram, and Pinterest, among other platforms. b.cause social media networks provide a wide range of statistics at elevated speeds and volumes, predicted b.g data techniques were applied in this study. This investigation studied user conduct on social networking sites using specific metrics and variab.es. This study examined user perception and attitudes towards social networking platforms. This study preprocessed data to remove outliers, disturb.nces, errors, and duplicate records, resulting in high-quality results. As a result, in this paper, an integrated system comb.ning sentiment evaluation and machine learning (ML) methods is constructed. This study utilized computational models and ML to anticipate user conduct on social networking platforms. The framework predicts user conduct on social networking platforms. Eighty percent of data has b.en employed for training, with twenty percent for testing. After testing many conventional and ensemb.e ML classifiers, decision trees outscored every other method.

关键词： Training Measurement Sentiment analysis Video on demand Social networking (online) Machine learning Market research Web.sites Multimedia communication Testing

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Automatic Speed Measurement and License Plate Reader for Motor Vehicles using CNN

Automatic Speed Measurement and License Plate Reader for Mot...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： D. Poornima N. Gopinath L Harish Manju Santosh Kumar C. Tamilselvi Department of Computer and Science Engineering Sathyabama Institute of Science and Technology Tamil Nadu India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of Information Science and Engineering CMR Institute of Technology Karnataka India Department of Electrical Engineering Government Engineering College Bihar India Department of Electrical and Electronics Engineering Gaya Colleg of Engineering Bihar India Department of Information Technology Dr. MGR Educational and Research Institute Tamil Nadu India

Assessing traffic surveillance data is a prevalent utilization of deep learning methodologies. Video processing techniques were employed to develop the system aimed at determining the velocity of vehicles. The analysis of video speed is conducted without the requirement of camera calib.ation. Frame sub.raction and masking techniques are employed to effectively segregate moving vehicles. The suggested system comprises four primary components. Ob.ect detection techniques are employed in the initial phase to identify and locate moving vehicles within a given footage. Real-time vehicle recognition leverages the utilization of R-CNN and deep learning techniques. In the sub.equent section, algorithms are employed to ob.erve the movement of ob.ects within individual video frames. Optical flow methods are employed in the third segment to ascertain the velocities of the respective vehicles. In the preceding part, the proposed work have utilized image processing techniques in conjunction with deep learning methodologies to accurately decipher the license plates of the automob.les under our surveillance. The experimental evaluation involving the utilization of traffic film demonstrated a system performance of 97% in precisely quantifying speed while achieving a license plate identification accuracy of 95%. The incorporation of state-of-the-art traffic management systems has facilitated the implementation of real-time speed monitoring and license plate reading capab.lities. This facilitates the deterrence of automob.le theft and the implementation of traffic regulations. Additionally, the methodology has the capab.lity to identify traffic patterns and pinpoint regions of congestion, thus assisting in the effective management of traffic.

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