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4th International Conference on Advances in Computing, communication Control and Networking, ICAC3N 2022

作者： Murugavalli, K. Ramalakshmi, R. Rajasekaran, M. Pallikonda Gandhi, Vaibhav Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Middlesex University Department of Design Engineering and Maths Education London United Kingdom

ISBN: (纸本)9781665474368

Undoubtedly one of the most important strands of the brain-computer interface (BCI) method is an alternate communication method via brain signals. BCI converts electroencephalogram (EEG) signals from a perception of activity in the brain into user action utilising software and hardware. BCI has piqued the interest of researchers in a wide range of disciplines, such as cognitive science, deep learning, pattern matching, drug treatment medicine, etc. Patients suffering from neuro and cognitive disorders can be assisted through BCI, potentially enabling communication via gestures or just mental imagination. In this paper, a novel combination of Discrete Wavelet Transform (DWT) for extracting the best features and Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) is adopted for classifying the EEG signals acquired during standing, walking and running on a treadmill. The dataset used is freely downloaded from Open science Framework repository. The proposed DWT-LSTMRNN method delivers 96.7% accuracy while classifying four different signals, and thus has the potential to be investigated further on BCI competition datasets that will pave way for a real-time application. © 2022 IEEE.

关键词： Discrete wavelet transforms

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Integrating Decision Trees with Cloud Computing for Predicting Cognitive Decline in Alzheimer’s Patients

Integrating Decision Trees with Cloud Computing for Predicti...

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Integrated Circuits and communication Systems (ICICACS), IEEE International Conference on

作者： K. Meenakshi Bathini Ravinder Mohammed Rehaman Pasha K. Thinakaran Dabbara Jayanayudu S.P. Maniraj Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Electronics and Communication Engineering Siddartha Institute of Science and Technology Puttur Andhra Pradesh India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

The proposed research work suggests a combined technique based on decision trees and cloud computing for predicting cognitive decline in Alzheimer’s patients. Medical records and data from wearable devices are among the many sources that perform preprocessing and feature engineering. Decision tree models can make the most efficient use of computing resources. Decision tree topologies are tested for performance criteria, including accuracy and F1 score. The system's predictive powers are rather encouraging since it frequently achieves accuracy rates over 90%. However, limitations in data quality and the intricacy of decision tree interpretation provide challenges. The prediction of cognitive deterioration in Alzheimer's patients may be made more scalable and interpretable by integrating decision trees with cloud computing. This method has promise for early identification and intervention despite its limitations. To further improve prediction accuracy and clinical value, future research should increase data quality and make it easier to understand.

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Plant Disease Prediction Using Convolutional Neural Networks

Plant Disease Prediction Using Convolutional Neural Networks

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： K. Sai Prasad K. Shekar P Chinnasamy Ajmeera Kiran Mohamed Junaid K A B Rachana Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Electronics and Communication Engineering R.M.K. Engineering College TamilNadu India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

This study demonstrates how to track the wellness of plants more accurately in general using deep learning along with the World Wide Web of Things. The key advantage of the article is that Convolution neural networks, an advanced neural network type, can forecast scenarios in plant environments by analyzing leaf pictures. In order to give farmers additional data, sensors that measure water in the soil, temperature and pressure are also used. The application informs the farmer on the agricultural industry. This study offers a workable technique that would enable farmers to remotely observe plant illnesses in their fields, saving them time and labor. In the agricultural sector, illnesses of leaves reduce the level of agricultural yield. As a consequence, in order to boost productivity in agriculture, leaf illness detection can be automated. Multiple leaf illnesses that impact detection sensitivity cause the majority of solutions fall short of objectives. This research establishes a model that encompasses image capture, extracting features, and image classification to build a computer vision framework. A neural networks classifiers called CNN is employed to categories Streaming images. According to experimental findings on jalapeno leaf rot being identified, the suggested technique has a higher identification rate than other existing methods. The categorization outcome tells whether the leaf is ill or not.

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Signal Detection and Channel Estimation in Multiple Input Multiple Output communication Systems using Distributed Approximate Message Passing

Signal Detection and Channel Estimation in Multiple Input Mu...

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Mobile Networks and Wireless communications (ICMNWC), IEEE International Conference on

作者： Layth Hussein Saranya. R Gummagatta Vybhavi Yajaman V. Malathy S. Senthil Kumar Department of Computers Techniques Engineering College of Technical Engineering the Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Bharathiyar Institute of Engineering for Women Salem India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering SR University Warangal India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In recent years, Multiple-Input Multiple-Output (MIMO) have revolutionized reliability and spectral efficiency of wireless communication systems. These systems exploit multiplexing and spatial diversity which enabling the essential for meeting the growing demands of modern wireless networks but it had challenges in detection of signals and estimation of channels. The traditional approaches for signal detection had faced challenges which include computational resources and network architecture. Therefore, this research proposes Distributed Approximate Message Passing (DAMP) for Signal Detection and Channel Estimation in MIMO communication systems. The collected data contains various digital and communication system which are pre-processed by using Deceived Non-Local Means (DNLM) and reduce the noise. These pre-processed data optimal features are extracted using Independent Component Analysis (ICA) with wavelet transform that separates mixed signals into independent components for eliminating the irrelevant attributes. Finally, Distributed Approximate Message Passing (DAMP) is employed that enables message passing framework to approximate the posterior distribution for detecting the signal and channel estimation in MIMO communication systems. The proposed DAMP succeeded with high results including accuracy (98.92%), precision (94.85%), recall (96.86%), F1-score (94.35%) when compared with existing Generative Adversarial Network (GAN).

关键词： Wavelet transforms Accuracy Message passing Wireless networks Noise Channel estimation Feature extraction Generative adversarial networks MIMO Signal detection

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A rainfall prediction system using Machine Learning (ML) and Internet of Things (IoT)

A rainfall prediction system using Machine Learning (ML) and...

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8th International Conference on Computing in engineering and Technology (ICCET 2023)

作者： P. Paul S. Basak A. Khan Department of Computer Science and Engineering University of Engineering and Management Jaipur Rajasthan-303807 India Department of Electronics and Communication Engineering University of Engineering and Management Jaipur Rajasthan-303807 India

One of the most pressing scientific and technical issues on a global scale is the difficulty of accurately predicting rainfall. Serious effects can result from significant changes in rainfall patterns, such as crop destruction from erratic heavy rain and danger to human life from catastrophic floods. It needs more accurate and dependable rainfall forecast techniques to get over these obstacles. This work presents an Internet of Things (IoT) and machine learning (ML)-based rainfall forecast system. As a note, this model used a modest amount of computer resources to predict rain, which is one of its benefits. In order to provide a more thorough evaluation, the performance of the proposed strategy is compared with that of many well-used techniques. The comparison shows that the random forest model outperforms the other ML models, which classify rainfall events with a higher accuracy of 94.68% and a low MSE.

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

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

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Emerging Research in Computational science (ICERCS), International Conference on

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

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

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.

关键词： Costs Decision making Predictive models Prediction algorithms Real-time systems Internet of Things Artificial intelligence Energy management Energy management systems Monitoring

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Hadoop based EMH framework: A Big Data approach

Hadoop based EMH framework: A Big Data approach

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2021 International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2021

作者： Khan, Mohammad Zunnun Kidwai, Mohd Suhaib Ahamad, Faiyaz Khan, Mohd. Usman Integral University Department of Computer Science and Engineering Lucknow India Integral University Department of Electronics and Communication Engineering Lucknow India

ISBN: (纸本)9781728177410

The terms like Gigabyte and megabyte have become old and outdated now because with the improvement in the field of storage technology, the storage abilities have increased and cost of storage of data has also been reduced. In present times terms like petabyte, terabyte, etc are in vogue as the e-storage capacity has increased worldwide. But at the same time, with improvement in the storage technology, the world now has ever increasing volume of data, so the need for storage is on the ***, huge amount of data is being generated and updated within a short span of time. Therefore, to take care of its storage, access and further advancement, analysis is required. But since the vast amount of data is getting generated in short span of time so the analysis of data as well as management of data has become a tedious, hectic and a challenging job for the data centers and *** issues and challenges are there while tackling data-related issues. For example, the data is available in different types such as structured, unstructured, semi-structured, heterogeneous, or homogeneous, which makes there sorting and storage difficult. Sometimes, the older tools are not sufficient to handle all these or a combination of one or more types in real-Time, or in distributed systems such as cloud *** creation of data and the aggregation of data is approximately in zettabytes per year. Such, volume of data is not only the problem for data management, but also affects the attributes like variety, velocity, value, and complexity. Then, there are also some technical issues as well related to the data transportation and ***, we need to think about the issues of storage and management of ever-increasing e-data and develop the novel perspective and innovative ways to address these *** this paper, we have analyzed these issues and challenges by studying different methodologies for big data analysis and design for Sensing and omitting medical data, e-Health

关键词： Data visualization

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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the *** energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of *** every sensor node has aﬁnite amount of *** power is the most signiﬁcant source in the *** is a well-known technique for enhan-cing the power feature in *** the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing *** using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire *** begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy *** Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the *** a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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Fingerprint definition for song recognition using Machine Learning Algorithm

Fingerprint definition for song recognition using Machine Le...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Arunakumari B N Shashidhar R Sahana B Jagadamba G A S Manjunath Roopa M Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India Dept. of Electronics & Communication Engineering JSS Science and Technology University Mysuru India Dept. of Electronics & Communication Engineering RV College of Engineering Bengaluru India Dept. of Information Science and Engineering Siddaganga Institte of Technology Tumkuru India Dept. Of Master of Computer Applications JSS Science and Technology University Mysuru India Dept. of Electronics & Communication Dayananda Sagar College of Engineering Bengaluru India

A condensed content-based signature that summarises an audio recording is known as an audio fingerprint. Audio The capability to recognize music regardless of design and without the use of meta-data or watermark embedding has made fingerprinting technologies popular. In. tegrity checking, watermark support, and content-based aural retrieval are additional applications for fingerprinting. The various methods of fingerprinting have been discussed using various justifications and terminologies, such as pattern matching, multimedia information retrieval, or cryptography. It is a distinct and condensed digest created using perceptually important elements of a video. Audio material can be monitored using fingerprinting technologies without requiring embedding of metadata or watermarks. However, audio fingerprinting has extra applications The algorithm is trained using thousands of mini audio speech commands. In order to improve the training efficiency, audio features are extracted during a pre-processing. The accuracy of the developed model is 83.7%.

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A Vulture Model Flapping - Wing with UAV Technology Based - A Novel Drone for Emergency and Safety Purpose Applications 3

A Vulture Model Flapping - Wing with UAV Technology Based - ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Shreepad, Sarange Raja, L. Thiyagesan, M. Khallikkunaisa Dwlvedi, Yagya Dutta Kumar, M Jogendra Ajeenkya DY Patil School of Engineering Department of Mechanical Engineering Maharashtra Pune412105 India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India R.M.K Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai601206 India HKBK College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telengana Hyderabad500043 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9781665462754

Vulcopter a leading way of innovation which gets its lifein near future. It works based on the principle of aerodynamics. Its principle plays a major role in UAV technology. The nameVulcopter because of the combination of the two words 'VULTURE' + 'COPTER'. This is because of its shape looks like vulture and hence the name. After studying deep with the topic of 'How Birds Fly' andother such related researches we have stepped into it to design the Vulcopter. Vulcopter paves its uniqueness with its shape andresembles the vulture and the four motors fixed at the each four corners will make the bird to lift upward and by making the aileron, elevator, rudder, throttle for justifying four components such as lift, weight, drag, thrust. © 2022 IEEE.

关键词： Birds

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