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International Conference on Advanced computer Control, ICACC

作者： M A Anitha K K Sherly Assistant Professor of Computer Science College of Engineering Cherthala Kerala India Associate Professor of Information Technology Rajagiri School of Engineering & Technology Kerala India

ISBN: (纸本)9781665439206

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.

关键词： Machine learning algorithms Redundancy Filtering algorithms Feature extraction Information filters Minimization Filtering theory

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Data hiding in whatsapp chats using horoscopes

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AIP Conference Proceedings 2024年第1期3168卷

作者： Monika Agarwal Assistant Professor Department of Computer Science and Engineering RKGIT Ghaziabad U. P. India

Steganography uses the technique of espionaging a media object in an another innocent looking file so that no one can doubt of its existence. Text steganography is a sub domain of steganography in the sense that it conceals the secret message in a text file. This article proposes a new way of hiding textual messages using steganography. The method uses Lagna chart of horoscope as well as Aadhar number of recipient to hide the message. Each character of the input message is masked to a value using digits in a random lagna Chart and recipient’s Aadhar number. Then this value is hidden under an emoticon using a pre-shared emoji table. Finally, cover message is created and sent via WhatsApp. The proposed approach also compares the results of various parameters like robustness, capacity ratio and security with some existing text steganographic methods.

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Optimizing Battery Management Systems For Electric Vehicles: Current Trends And Future Directions

Optimizing Battery Management Systems For Electric Vehicles:...

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Global Conference for Advancement in Technology (GCAT)

作者： V.S.Prakash Annamalai Muthu B. Anandan V. Velmurugan T. Alex Stanley Raja Purushothaman V Department of Computer Science Kristu Jayanti College Bengaluru Electrical Section Engineering Department University of Technology and Applied Science Nizwa Oman Department of Mechanical Engineering Siddharth Institute of Engineering & Technology Puttur Andhrapradesh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Assistant Professor Level III Bannari Amman Institute of Technology Sathyamangalam Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid global shift towards electric vehicles (EVs) marks a transformative era in transportation, driven by the imperative to reduce carbon emissions and reliance on fossil fuels. Central to the performance and longevity of EVs are Battery Management Systems (BMS), which optimize battery usage, enhance safety, and extend operational life. This comprehensive review explores current trends and future directions in BMS optimization, focusing on key technological advancements and their implications for electric mobility. Battery Management Systems (BMS) serve as critical components in electric vehicles, orchestrating the efficient operation of battery packs through real-time monitoring, state-of-charge (SoC) estimation, thermal management, and fault diagnosis. These systems not only ensure optimal performance but also safeguard against potential hazards, thereby enhancing vehicle reliability and user confidence. Current advancements in BMS technology emphasize enhanced accuracy in SoC estimation, leveraging sophisticated algorithms and sensor integration to provide precise information on battery charge levels. Coupled with innovative thermal management strategies, which include active cooling and heating systems, BMS can mitigate thermal stress and optimize battery performance across diverse environmental conditions. By synthesizing technological advancements, emerging trends, and potential challenges, it offers valuable insights for researchers, engineers, and policymakers aiming to advance the efficiency, reliability, and sustainability of electric mobility solutions worldwide.

关键词： Technological innovation Electric potential Battery management systems Estimation Electric vehicles Thermal management Market research Sustainable development Thermal stresses Optimization

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AI Framework for Keys Instruction and Music Notation

AI Framework for Keys Instruction and Music Notation

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Krishnaraj Rao N S Smitha GL P. S. Ramesh Nandhini T J P. Nagaveni A Balaji Assistant Professor Department of Information Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology (NMAMIT) Nitte Karnataka India Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Science (SIMATS) Chennai India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha School of Enginnering Saveetha Institute of Medical and Technical Science – SIMATS Saveetha University Chennai India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of IT Panimalar Engineering College

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The study of artificial intelligence focuses on the principles that guide all human intellectual endeavors. The integration of several academic disciplines, including as computer science, philosophy, politics, linguistics, systems engineering, neurophysiology, and behavioral science, has allowed it to grow. This article will describe how to build an intelligent machine, consisting of both hardware and software, that can replicate and exceed the abilities of the human brain in order to handle complicated issues that need specialized knowledge. It will specifically examine how to more effectively use artificial intelligence support systems into piano instruction. An Introduction, an oral language and music-assisted education system based on the ARM and SA algorithm, is proposed by this study in order to ascertain the implementation principle guiding the functioning of the piano automation recording system. It also delves deeply into the theory and operation of automated music recording systems, compiling and evaluating data that these systems generate via internal databases. is an effort to improve music education via the use of artificial intelligence support systems. The results of the experiment show that the training system for the automated notation algorithms for playing the piano and songs has a 56.81 and fast growing machine learning auxiliary technology utilization rate. Thus, we may infer that using an AI support system is necessary while teaching performance piano and automated notation.

关键词： Training Machine learning algorithms Education Music Machine learning Software Recording Multiple signal classification Telecommunication computing Artificial intelligence

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Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach 4

Predicting Price Direction of Bitcoin based on Hybrid Model ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Karahyla, Jai Kishan Sharma, Neelam Chamoli, Sushant Shirgire, Anil Kant, Ravi Chauhan, Amit Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University Human Rights HaryanaMullana Ambala India Lovely Professional University Physical Education Punjab Phagwara India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Dr D y Patil Institute of Technology Department of Civil Engineering MaharashtraPimpri Pune India Shoolini University Cse Department Himachal Pradesh Solan India Department of Life Sciences Karnataka Bengaluru India

ISBN: (纸本)9798350300093

Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. © 2023 IEEE.

关键词： Bitcoin

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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A novel prediction of remaining useful life time of rolling bearings using convolutional neural network with bidirectional long short term memory

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Balamurugan, R. Takale, Dattatray G. Parvez, M. Muzammil Gnanamurugan, S. Associate Professor Department of Mechanical Engineering M.Kumarasamy College of Engineering Karur India Department of Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Assistant Professor/ ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Avadi Chennai India

Detection of bearing problems increases the importance of the service life of rotating machinery. Convolutional neural networks (CNNs) are often used in current research, and databases built on deep learning (DL) models have improved capabilities in the field of defect diagnosis. We use the publicly available Case Western Reserve University (CWRU) dataset to compare the classification accuracy and gain more adaptive knowledge and insights about the proposed approach. Extensive tests and evaluations are performed on the dataset to verify the diagnostic effectiveness of the recommended method in different situations. To demonstrate the superiority of the proposed method, we compare multiple views of the same dataset with similar tasks. CNN supports degraded index sequences to reduce noise and stop temporal oscillations. A new CNN-BiLSTM model is used to capture current and historical inspection data and predict the RUL's service life and supported power levels. Regarding production, we follow health rates. The proposed method was evaluated by accelerating the bearing motion to failure, and the results demonstrated its advantages in terms of more accurate RUL prediction. According to the experimental results, the proposed center distance measurement method is a new and valuable means for intelligent bearing diagnosis. Experimental results using 48 K and 12 K CWRU datasets show that the overall accuracy of the BiLSTM method is 99.80% and 98.3%, respectively, which is better in diagnosis than some popular models. © 2024 The Authors

关键词： BiLSTM CNN CWRU dataset Rolling bearings life time

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Lip reading using deep learning and convolutional neural network

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AIP Conference Proceedings 2024年第1期3134卷

作者： Elizabeth Isaac Sachin K. Tomy Basil Joy Edwin Augustine Thomas Hussain Nizar Ahmed Jacob Raybin Assistant Professor Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Lip reading involves deciphering and scrutinizing a speaker’s lip movements to understand spoken words or phrases. The diverse factors like speech pace, intensity, and similar character sequences pose significant challenges to lip reading. This study introduces a lip-reading model tailored for video data containing variable-length sequence frames without audio. Initially, we isolate the lip region from each facial image within the video sequence and amalgamate them into a unified image. Subsequently, a twelve-layer convolutional neural network with two layers of batch normalization is constructed to train the model and extract visual information. This approach aims to mitigate both internal and external disparities encompassing traits such as speaker accent, lighting conditions, picture frames, speaking tempo, and posture. By leveraging extensive data, the model demonstrates the capability to accurately anticipate complete words.

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Unveiling deceit: A computer vision-based approach to detect lies from facial expressions

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AIP Conference Proceedings 2024年第1期3134卷

作者： Elizabeth Isaac Pristy Paul Thoduparambil M. Raghu Rohitha Anuvindha Ansalam T. P. Akshay Assistant Professor Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science and Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Polygraph tests monitor heart rate and pulse for truth determination but aren’t fully reliable. Face based deception detection systems are noninvasive and are more convenient. The proposed system uses computer Vision to monitor eye blink rate, gaze direction, lip compression, and pupil dilation to determine truth accuracy in real-time. The number of frames with a threshold value lower than the Eye-Aspect-Ratio (EAR) constant determines the eye blink count. The pupil coordinates are used to track the eye gaze direction. The eye centroid to iris contour maximum distance tracks pupil dilation. Lip compression is determined by measuring the distance between the mean of upper and lower lip landmarks. The weighted sum method is used to find the lie index from the obtained features. The true positive rate (TPR) and false positive rate (FPR) at various threshold values is calculated and an ROC (Receiver Operating Characteristic) curve is plotted using these values. The point on the curve with the highest TPR and lowest FPR determines the optimal threshold for the lie detection system. This method allows customization of the system’s performance to meet specific requirements.

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Keyword search application for videos using frames

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AIP Conference Proceedings 2024年第1期3134卷

作者： Anu Eldho Linda Sara Mathew B. Rohit Eldhobasil Parackal Balu Felix Assistant Professor Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India Department of Computer Science & Engineering Mar Athanasius College of Engineering Kothamangalam Kerala India

Keyword search in videos is a technique that allows for efficient search and retrieval of specific moments within a video. This is accomplished by using speech recognition technology to convert audio from the video into text. This text can be compared with the visual content of each frame in the video using optical character recognition (OCR) technology. By iterating through each frame and comparing the audio and visual text, matches can be identified and the relevant parts of the video can be quickly accessed using the corresponding timestamps. We use a video analysis tool or library that has the ability to extract timestamps from a video file. These tools can be used to create a list of timestamps for all the frames in a video, which can then be used to quickly access specific moments in the v ideo. This technique can be useful in a variety of applications, such as media search, video editing, and video surveillance.

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