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Data hiding in whatsapp chats using horoscopes

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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Game theory-based optimization for efficient IoT task offloading in 6G network base stations

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Measurement: Sensors 2024年 33卷

作者： Keshta, Ismail Soni, Mukesh Deb, Nabamita singh, Shweta Saravanan, K. Raza Khan, Dr Ihtiram Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Assistant Professor Department of Information Technology Gauhati University India Department of Information Technology R.M.D. Engineering College Chennai India Computer Science Department Jamia Hamdard Delhi India Electronics and communication department IES college of technology Bhopal India

This study aims to optimise computing for intricate jobs within the overlapping coverage of 6G network base stations. A multi-access edge computing network model is created by solving the issues of task offloading. This model involves many base stations and IoT devices. It takes into account factors such as task delay, energy consumption, societal impacts, and economic incentives. The objective of joint optimization is to maximize the revenues of base stations and the utilities of IoT devices by considering base station price, IoT device base station selection, and job offloading mechanisms. The IoT device base station selection is addressed using a many-to-one matching game model, while pricing and task offloading interactions are managed by a two-stage approach based on Stackelberg game theory. The suggested Optimal Pricing and Best Response Algorithm (OBGT), which is based on Game Theory, successfully reaches equilibrium solutions. It demonstrates rapid convergence in simulations and improves both base station profits and IoT device utility. This study integrates data-driven mobile computing systems assurance for futuristic smart vertical networks, enhancing the efficiency of task offloading and optimization. © 2024 The Authors

关键词： Game theory

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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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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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Multimodal search in fashion store using CLIP

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AIP Conference Proceedings 2024年第1期3134卷

作者： Isaac Elizabeth Anu Mathew Joby Roy Meckamalil Rotney Sagar V. K. Sruthi N. Kartha Sreekanth P. M. Sreelakshmi 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

Contemporary computer vision systems are typically trained to recognize a limited set of predetermined object categories. This restricted type of supervision limits their applicability and generalization since additional labeled data is needed to indicate any other visual concept. A potential approach that leverages a broader source of supervision involves learning directly from raw text about images. The proposed solution involves implementing a multimodal search in a fashion store using the Contrastive Language Image Pretraining (CLIP) model, which enables vector-based search rather than syntax-based search. CLIP improves product visibility and assists users in obtaining better search results that match their preferences. It is demonstrated that a simple pre-training task of predicting the corresponding image for a given caption is an effective and scalable technique for learning state-of-the-art image representations from scratch using a dataset of (image, text) pairs collected from the internet. Natural language is employed during pre-training to refer to learned visual concepts or describe new ones, enabling zero-shot transfer of the model to downstream tasks. Integration with clustering techniques on the vector space further enhances CLIP’s capabilities, facilitating efficient and accurate retrieval of items. By grouping similar fashion items based on visual similarity and semantic relationships, the system enables efficient and accurate retrieval of relevant items. The model can generalize effectively to most tasks without requiring any training on a specific dataset and is frequently on par with a fully supervised baseline.

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Automated data preprocessing and training interface for machine learning applications

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AIP Conference Proceedings 2024年第1期2942卷

作者： Anagha Challa Akhila Vutukuri Maithreyi Kenguva Sateesh Kumar Kanagala Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India Assistant Professor Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

Machine learning is a promising tool for analyzing and predicting data. However, it is very time-consuming and challenging to manually process the data. There are also many missing values in the data that can affect the accuracy of the models. We propose an automated approach to pre-process the data. It efficiently performs the various steps involved in the data cleaning process by converting the categorical values to a label encoded value, sampling the data, and replacing the missing values with the appropriate central tendency. This project takes the raw data as input and provides a clean error-free dataset that is suitable for training machine learning models. It also aims to use a web application to automate data pre-processing and training of classification and regression models. It uses multiple algorithms and normalization strategies to get the optimum model based on the desired metric. It allows users to download the clean dataset as well as the trained models after the models have been trained. Multiple datasets, including binary classification and regression, are used to test this model.

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Deep Learning and its Applications: A Novel Approach to Machine Learning with Encoding Algorithms

Deep Learning and its Applications: A Novel Approach to Mach...

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Intelligent & Innovative Practices in engineering & Management (IIPEM), International Conference on

作者： Kamalesh Thoppaen Suresh Babu Bhagyashree Ashok Tingare Vijayshri Khedkar Tarun Dhar Diwan P. William Abhishek Badholia Globallogic Inc Santa Clara CA USA Department of Artificial Intelligence and Data Science D Y Patil College of Engineering Akurdi Pune Department of Computer Science & Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Assistant Professor and Controller of Examination (COE) Atal Bihari Vajpayee University Bilaspur India Deaprtment of Information Technology Sanjivani College of Engineering Kopargaon Amity University Dubai UAE Department of Data Science Shri Shankaracharya Institute of Professional Management and Technology Raipur

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

Deep learning has revolutionized data processing by recognizing complicated patterns using multi-layered neural networks. Encoding complex data like hyperspectral photos may help deep learning models perform better. Encoding shapes raw data for compression, dimensionality reduction, and feature extraction. These methods matter. This research found that higher-dimensional encodings can improve deep learning model accuracy and convergence. Complex encoding algorithms like FE-16D and BLOSUM-20D outperform simpler ones in validation accuracy and AUC. To prove this, extensive testing and comparison were done. The study emphasizes the necessity to combine deep learning frameworks with improved encoding techniques to improve machine learning and large-scale data processing.

关键词： Deep learning Dimensionality reduction Machine learning algorithms Computational modeling Neural networks Feature extraction Data processing Encoding Data models Hyperspectral imaging

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