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International Conference on science Technology engineering and Management (ICONSTEM)

作者： B. Manimaran M. Tamilselvi Sanasam ChanuInunganbi Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology (Ramapuram Campus) Chennai India Department of Computer Science and Engineering KoneruLakshamiah Education Foundation Vijayawada India

Solar energy is the most available renewable energy source unlike the energy sources and it is the immaculate energy source. Everyone can make it especially as it is providing tariff free electricity and power in long term investment plan. These are two different technologies to generate the electricity using solar photovoltaic (PV) and concentrated solar power (CSP) system. The contactless energy transfer is always used to transfer the energy from source to load without any physical connection. Natural fuels have declined as usage of fuels has increased day by day. And so, the world is moving forward to electric vehicles. In the fast paced would of technology, the electric buses will become mandatory and efficient for long driver in future years. The main idea is to combine the solar energy for hazard and hassle-free supply using contactless energy transfer (CET). The CET is system is combined with solar energy source for mobile charging. And the additional solar energy can be stored in battery electric bus. Hence the CET system removes the distribution cables and it will be safer, comfort, satisfactory and adaptable.

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Smart Detection of Passive Smokers using Machine Learning

Smart Detection of Passive Smokers using Machine Learning

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International Conference on Computing communication Control and Automation (ICCUBEA)

作者： Pratham Shah Astha Parekh Divyansh Bajaj Debabrata Swain Manish Kumar Amol Bhilare Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Department of Electronics and Communication Engineering Pandit Deendayal Energy University Gandhinagar India Computer Engineering Department Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This has become the source of a great concern, especially amongst the young generation. Machine Learning is used to find a pattern of biological attributes to identify possible smokers. The process of teaching a machine to do intelligent classification from a sizable archive of historical data is known as machine learning. The aim of this paper is to develop a learning model that can detect passive smokers through features like eyesight, weight, fasting blood sugar, cholesterol and more. On this dataset, a number of data preprocessing operations are carried out to build a reliable model which include checking for null values , encoding the data , balancing the dataset and scaling it and selection of highly correlated features. A variety of supervised learning models are employed in the model training process to create a robust machine learning model. From the set of models applied, K-Nearest Neighbour (KNN) (84 %) and Random Forest (RF) (86 %) gave the highest accuracy. However, implementing Random Forest with Hyperparameter tuning using GridSearchCV resulted in a higher accuracy (87 %).

关键词： Radio frequency Training Accuracy Supervised learning Null value Feature extraction Encoding Tuning Random forests Diseases

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A MultiModal Neural Network Architecture for Document Image Classification

A MultiModal Neural Network Architecture for Document Image ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： S Krithika A R Priyadharshini G Bharathi Mohan M Gayathri Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Document classification is a crucial domain in natural language processing that involves classifying text documents into predefined categories. However, in multiple cases, the documents are heterogeneous with different modalities such as images and flow diagrams, giving rise to the need for handling multiple modalities. Thus, multimodal document classification enables a more comprehensive understanding of document content by incorporating multiple modalities such as text and images. This enhances information extraction and enables more accurate categorization of documents, benefiting various applications such as content organization, search, recommendation systems, and information retrieval. This work proposes a deep learning approach that leverages both textual and image information for improved classification of multimodal documents. Our multimodal deep neural network architecture utilizes two different channels in processing the multimodal features: text and document images. The textual model leverages BERT to generate embeddings of the text, while the image feature extraction channel employs a hybrid architecture consisting of the pre-trained convolutional neural network model VGG-19 as the backbone followed by a vision transformer. The two different channels are incorporated into a unified framework that performs the final classification. The training and testing of the proposed framework were conducted on the benchmark TOBACCO-3482 dataset, demonstrating the effectiveness and potential of our proposed multimodal classification approach. The TOBACCO-3482 dataset is a widely used benchmark dataset for document image analysis and classification. The effectiveness in performing the classification of the proposed framework is evaluated on the grounds of performance metrics such as confusion matrix, accuracy, precision, recall, and F1-score.

关键词： Training Accuracy Text analysis computer architecture Organizations Benchmark testing Information retrieval Transformers Natural language processing Recommender systems

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Colour Image Cryptography using Chaotic Pixel Shuffling and 3D Logistic Diffusion 4

Colour Image Cryptography using Chaotic Pixel Shuffling and ...

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4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Ravi, Renjith V Goyal, S.B. Singla, Sanjay Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India Faculty of Information Technology City University Petaling Jaya Malaysia Department of Computer Science and Engineering Uie Chandigarh University Punjab Mohali India

ISBN: (纸本)9781450399937

All types of multimedia data, including digital images, audio, text, and video, are now easily accessible on the internet due to the phenomenal growth of internet services and wireless telecommunication systems, including mobile and computer systems, in order to achieve a specific degree of security, integrity, and secrecy as well as to prevent malicious access to the information. In contrast, for data storage and transmission, cryptographic procedures are necessary. This study presents brand-new, very effective colour image encryption and decryption algorithms that use diffusion and pixel scrambling. At first Zig-Zag permutation was used to scramble the data. Then, by concealing the statistical makeup of the pixels, the two-dimensional Lozi map is used to distort further the connection between neighbouring pixels in the original image. Furthermore, the diffusion in three colour channels is accomplished using a 3D logistic map. During the decryption process, the image pixels are again moved back to their original positions. The chaotic mapping technique encrypts images to reduce the correlation between encrypted and unencrypted versions. As described in this study, the tested and examined encryption algorithm on certain colour photos showed substantial security and authenticity to resist statistical and differential cyber attacks. © 2022 ACM.

关键词： Pixels

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AI/ML Applications in Spoofing Detection in 5G Networks: Challenges and Solutions

AI/ML Applications in Spoofing Detection in 5G Networks: Cha...

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Intelligent and Innovative Technologies in Computing, Electrical and electronics (IITCEE), International Conference

作者： Monika Singh Raveesh Hegde Mahesh Kumar Jha Amar Choudhary Rubini P Electronics and Communication Engg. CMR Institute of Technology Bengaluru India Electronics and Communication Engg. Alliance College of Engineering and Design Alliance University Bengaluru India Computer Science and Engg. School of Engineering and Technology CMR University Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

5G networks has advanced connectivity, high data, low latency and reliability. Along with these features new challenges come with respect to security, particularly spoofing attacks. One of the necessity in 5G and future wireless communication is to prevent the spoofing attacks depending on the applications. Artificial Intelligence (AI) and Machine Learning (ML) present promising solutions for detecting and mitigating spoofing threats in 5G networks. This paper explains the different types of spoofing and applications, challenges, and future prospects of AI/ML in addressing spoofing in 5G networks. AI/ML algorithms can be used in detection of the location of attackers. This paper also analyses current methodologies, highlight key challenges, and discuss potential advancements that could enhance the security of 5G networks.

关键词： Wireless communication Industries Quantum computing Machine learning algorithms 5G mobile communication Prevention and mitigation Real-time systems Security Reliability Low latency communication

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Stock Market Analysis and Price Prediction

Stock Market Analysis and Price Prediction

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： Rahul S Shrinivas V N Brindha G Karthikeyan M V Computer Science and Engineering St.Joseph's college of Engineering Chennai Tamil Nadu India Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

This Study presents a new approach to stock price analysis and prediction by combining several methodologies: fundamental analysis, logistic regression, technical analysis through the k-Nearest Neighbors (KNN) algorithm, and price forecasting through the application of LSTM neural networks. The first approach was to use fundamental analysis to determine intrinsic values for stocks based on financial health and market conditions. Market trends and patterns will be captured by performing technical analysis on KNN algorithms that identify similarities in historical data points. Core to the price prediction, it would depend on the LSTM networks which are powerful in modeling time-series data and could be able to capture complex and non- linear relationships found in the movements of the stock price. All the strengths mentioned above will have a great deal in improving the accuracy of the predictions of this proposed model. Experimental results indicate that the integrated model is much more reliable and precise than the traditional stock prediction methods. Therefore, this study is aimed at examining the potential for using machine learning and advanced statistical techniques to enhance forecasting capabilities in stock market analysis to enable investors and analysts with better decision-making tools through more powerful analysis.

关键词： Logistic regression Accuracy Machine learning Predictive models Nearest neighbor methods Prediction algorithms Data models Stock markets Forecasting Long short term memory

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Guest editorial

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International Journal of Intelligent Unmanned Systems 2022年第1期10卷 1-2页

作者： Bindhu V, V. Chen, Joy Iong-Zong Ahmad, Badrul Hisham Bin Khan, Faizal Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore India Dayeh University Changhua Taiwan Department of Electronic and Communication Engineering Universiti Teknikal Malaysia–Main Campus Durian Tunggal Malaysia Department of Computer Science Shaqra University Al-Dawadmi Saudi Arabia

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Hybrid Threshold Speech Enhancement Scheme Using TEO And Wavelet Coefficients

Hybrid Threshold Speech Enhancement Scheme Using TEO And Wav...

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Electrical, electronics, Information and communication Technologies (ICEEICT), International Conference on

作者： Latha R Suhas A R B P Pradeep Kumar M.Mohammed Ibrahim Sathiyapriya V Department of Electronics and Communication Engineering HKBK College of Engineering Bangalore India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India

Speech Enhancement (SE) aims to improve the quality of degraded speech while maintaining its intelligibility. The Wavelet Transform (WT) has become a powerful tool of signal analysis thereby widely used in signal detection and signal denoising. In this paper, we propose an effective means of SE by a hybrid threshold scheme using WT. The proposed methodology looks into both falling the noise and preserving edges of the speech signal unlike the conventional Hybrid Threshold (HT) and Soft Threshold (ST) in the wavelet domain. The threshold value in the wavelet domain is maintained constant for all sub-bands of the signal which reduces denoising efficiency. A novel speech augmentation technique built on the wavelet onsets and time adaption of introduced by calculating wavelet coefficients of the Teager Energy. Performance analysis of speech enhancement techniques using Wavelet coefficients and Teager Energy Operator (TEO) with hybrid threshold method is done. The experiment is carried out for speech data with various values of SNR vacillating from -10 to +10 db with Additive White Gaussian Noise (AWGN).

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Energy Efficient Cluster-Based Optimal Resource Management in IoT Environment

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computers, Materials & Continua 2022年第1期70卷 1247-1261页

作者： J.V.Anchitaalagammai T.Jayasankar P.Selvaraj Mohamed Yacin Sikkandar M.Zakarya Mohamed Elhoseny K.Shankar Department of Computer Science and Engineering Velammal College of Engineering and TechnologyMadurai625009India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Information Technology SRMInstitute of Science and TechnologyKattankulathur603203Tamil NaduIndia Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAlMajmaah11952Saudi Arabia Department of Mathematics College of ScienceKing Khalid UniversityP.O.Box 9004Abha61413Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityAssiut71524Egypt College of Computer Information Technology American University in the EmiratesDubai503000United Arab Emirates Department of Computer Applications Alagappa UniversityKaraikudiIndia

Internet of Things(IoT)is a technological revolution that redefined communication and computation of modern *** generally refers to a network of gadgets linked via wireless network and communicates via *** management,especially energy management,is a critical issue when designing IoT *** studies reported that clustering and routing are energy efficient solutions for optimal management of resources in IoT *** this point of view,the current study devises a new Energy-Efficient Clustering-based Routing technique for Resource Management i.e.,EECBRM in IoT *** proposed EECBRM model has three stages namely,fuzzy logic-based clustering,Lion Whale Optimization with Tumbling(LWOT)-based routing and cluster maintenance *** proposed EECBRMmodel was validated through a series of experiments and the results were verified under several *** model was compared with existing methods in terms of energy efficiency,delay,number of data transmission,and network *** simulated,in comparison with other methods,EECBRM model yielded excellent results in a significant ***,the efficiency of the proposed model is established.

关键词： IoT environment clustering routing resource management energy efficiency

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IoT Enabled Waste Management Optimization Framework (IWMOF)

IoT Enabled Waste Management Optimization Framework (IWMOF)

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： R. Thirupathieswaran K. Palanivel Rajan R. Niranjana A. Essaki Muthu R. Santhana Krishnan K. Saravanan Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department Computer Science and Engineering Anna University Regional Campus Tirunelveli India

Globally, the growing amount of waste produced by cities is of great concern due to its negative effects on both the surroundings and the well-being of people. Waste management has emerged as a crucial topic of study to address this problem, with numerous strategies and methods proposed to improve waste collection, transportation, and disposal. In this research, a novel framework for waste management optimization in IoT (Internet of Things) environments, using Deep Reinforcement Learning with Fuzzy Logic Control is proposed. The framework employs IoT sensors to gather real-time data on waste generation, bin levels, and truck locations. Fuzzy logic control is used to transform the sensor data into actionable insights that aid in decision-making. Deep Reinforcement Learning is then utilized to optimize waste collection routes and schedules, reducing transportation and collection costs while mitigating negative environmental impacts. To assess the effectiveness of the suggested framework, simulations are executed using real-world information. The simulation results show that the framework was effective in reducing the total collection time, minimizing the distance travelled by the trucks, and maximizing the waste collection efficiency. The proposed framework also demonstrated significant improvements in reducing the negative impacts of waste management on the environment, such as reducing greenhouse gas emissions and minimizing the number of waste transportation trips. The proposed framework offers an innovative approach to optimize waste management using the power of Fuzzy logic control and Deep Reinforcement Learning. The use of IoT sensors and real-time data makes it possible to achieve a more efficient and sustainable waste management system. The results of simulations demonstrate the potential of the proposed framework to be implemented in real-world waste management systems to enhance their efficiency and effectiveness.

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