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Amino Acid Encryption Method Using Genetic Algorithm for Key Generation

computers, Materials & Continua 2022年第1期70卷 123-134页

作者： Ahmed S.Sakr M.Y.Shams Amena Mahmoud Mohammed Zidan Department of Information System Faculty of Computers and InformationMenofia UniversityEgypt Department of Machine learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computers and InformationKafrelshiekh UniversityEgypt Hurghada Faculty of Computers and Artificial Intelligence South Valley UniversityEgypt

In this new information era,the transfer of data and information has become a very important *** data must be kept secured from unauthorized persons using *** science of cryptography depends not only on complex mathematical models but also on encryption *** acid encryption is a promising model for data *** this paper,we propose an amino acid encryption model with two encryption *** first key is generated randomly using the genetic *** second key is called the protein key which is generated from converting DNA to a protein ***,the protein message and the first key are used in the modified Playfair matrix to generate the cypher *** experimental results show that the proposed model survives against known attacks such as the Brute-force attack and the Ciphertext-only *** addition,the proposed model has been tested over different types of characters including white spaces and special characters,as all the data is encoded to 8-bit *** performance of the proposed model is compared with other models using encryption time and decryption *** model also balances all three principles in the CIA triad.

关键词： Cryptography amino acid genetic algorithm playfair deep learning DNA computing

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Visual Computing Unified Application Using Deep learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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Passive RFID Encryption and Decryption using CLI 7

Passive RFID Encryption and Decryption using CLI

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Suhas Kumar, S.J. Satyaa, Sudarshan Vishwamalyan, J.S. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350341485

The implementation of the concept of RFID encryption to make a system that prevents the attack on the data of any user or organization on the cloud. The built application has a command line interface. The command line interface is used here instead of a web server to prevent data leakage during data transmission and reduce the risk of a rogue web server. The command line interface takes in an input image and encrypts it, producing an encrypted image and a key file. The CLI also decrypts the previously encrypted image using the key file. This app as a whole solves the problem of users and organizations that store images on the cloud, using this app the user can store encrypted images on the web while the low storage key file is on the user/organization's on-site server. This provides a 2-tier security mechanism via which an adversary will need to hack both the organization server and the cloud server to compromise the data, this way the image data is secure without any additional cost or overhead to the organization. an RFID encryption algorithm is developed along with a non-network-based CLI that secures the organization's RFID data and helps implement a secure cloud-bursting architecture. In this project, an algorithm is developed by combining both the XOR-based encryption algorithm and non-network-based CLI to secure the RFID of the user/organization. Most encryption algorithms nowadays use web-based servers to encrypt the RFID and produce the key file. This makes web-based encryption insecure and vulnerable to a man-in-the-middle attack. Therefore, this app comes to the rescue as it provides a non-network-based CLI by which there is no need for the actual RFID to go outside of the local machine, thus making this app and its architecture secure and robust. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Real-Time Potholes Detection and Prevention Using Deep learning Techniques 8th

Real-Time Potholes Detection and Prevention Using Deep Learn...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Shabai, Iqbal Mahalle, Parikshit N. Shinde, Gitanjali Department of Information Technology Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune India Department of Artificial Intelligence and Machine Learning Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9789819766802

Road infrastructure safety and maintenance have received more attention recently due to the significant influence that it has on traffic flow and road user safety. Potholes are one common kind of road defect that seriously endangers drivers. Manual checks take a lot of time and may delay necessary repairs. In response to these challenges, a real-time pothole recognition system utilizing state-of-the-art computer vision and deep learning techniques was developed. The system leverages the Faster R-CNN model for precise pothole identification, enabling fast detection and timely repairs. The automated detection and instantaneous notification of authorities by the system enhances road safety and maintenance efficiency. This ingenious tactic improves the standard of the transportation network and creates roads safer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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artificial intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network 1

Artificial Intelligence powered chatbots for the speech disa...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mukherjee, Subhayan Chatterjee, Anusuya Chandrakar, Khushbu Saxena, Sandeep Asansol Engineering College Artificial Intelligence and Machine Learning Asansol India Asansol Engineering College Electronics and Communication Engineering Asansol India Asansol Engineering College Computer Science and Engineering Asansol India Greater Noida Institute of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350356816

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled 'artificial intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network' provides a novel integration of artificial intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities. © 2024 IEEE.

关键词： Assistive technology

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DeepDerm: Elevating Precision in Dermatological Diagnosis with Enhanced CNN 15

DeepDerm: Elevating Precision in Dermatological Diagnosis wi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mundada, Monica R. Seema, S. Sowmya J, B.J. Student, Sonia Fathima Ramaiah Institute of Technology Department of Masters of Compiuter Application Banglore India Ramaiah Institute of Technology Department of Computer Science and Engineering Banglore India Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Banglore India

ISBN: (纸本)9798350370249

Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings requires dermoscopic analysis and clinical screening due to medical criteria. It gets more difficult to classify skin diseases using medical photographs because of the complex forms, wide range of colors, and data security issues. In this study, we offer a dependable approach for the classification of 20 distinct types of skin illnesses, based on an improved Convolutional Neural Network (CNN) model. Our enhanced CNN design exceeds the standard CNN, which only achieves an accuracy of 31.38%, with an astounding accuracy of 98.21%. The accuracy of two benchmark algorithms, AlexNet and VGG19, is also not particularly high (32.14% and 52.30%, respectively). In contrast to traditional CNN models the enhanced CNN performs better, demonstrating its efficacy and dependability in accurately identifying a wide range of skin disorders. including well-known architectures like AlexNet and VGG19. Positive implications of this discovery include the development of more precise diagnostic tools for dermatology. © 2024 IEEE.

关键词： Benchmark Algorithms CNN Enhanced CNN Skin Disease Classification

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Wave Height Prediction Suitable for Maritime Transportation Based on Green Ocean of Things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2023年第2期4卷 328-337页

作者： Lou, Ranran Lv, Zhihan Guizani, Mohsen Qingdao University College of Computer Science and Technology Qingdao266071 China Uppsala University Faculty of Arts Department of Game Design Uppsala75002 Sweden Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi51133 United Arab Emirates

Nowadays, the application fields of the Internet of Things (IoT) involve all aspects. This article combines ocean research with the IoT, in order to investigate the wave height prediction to assist ships to improve the economy and safety of maritime transportation and proposes an ocean IoT Green Ocean of Things (GOoT) with a green and low-carbon concept. In the wave height prediction, we apply a hybrid model (EMD-TCN) combining the temporal convolutional network (TCN) and the empirical mode decomposition (EMD) to the buoy observation data. We then compare it with TCN, long short-term memory (LSTM), and hybrid model EMD-LSTM. By testing the data of eight selected NDBC buoys distributed in different sea areas, the effectiveness of the EMD-TCN hybrid model in wave height prediction is verified. The hysteresis problem in previous wave height prediction research is eliminated, while improving the accuracy of the wave height prediction. In the 24 h, 36 h, and 48 h wave height prediction, the minimum mean absolute errors are 0.1265, 0.1689, and 0.1963, respectively;the maximum coefficient of determination are 0.9388, 0.9019, and 0.8712, respectively. In addition, in the short-term prediction, the EMD-TCN hybrid model also performs well, and has strong versatility. © 2020 IEEE.

关键词： Empirical mode decomposition

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Self Defence and Safety Monitoring System 4

Self Defence and Safety Monitoring System

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

作者： Suhas, M.L. Kashyap, Akshat Devadiga, Dhanush Ghoshal, Manish Roopashree, S. Deemed-to-be University Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Jain Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

In the modern world, the safety of women has been compromised. Domestic violence, sexual assault, and murder against women are more common than they are supposed to be. Women's safety is a major concern in India and there are many laws and measures in place to stop them, but the number of such incidents is only increasing by the day. This is not just limited to women as men face a lot of harassment too. In response to these problems, we aim to create a smart safety device that provides some security to the user. Many such smart devices are already available in the market but are not proven to be effective and often provide serious damage to the attacker. We aim to create a safety device that is capable of alerting the user or taking actions automatically without or with very little involvement from the user to activate it. This helps when there is a situation where the user doesn't get enough time to react. Different models available are analysed and the proposed solution is explained in detail. The drawbacks and future works are also included in the article. © 2022 IEEE.

关键词： Emotion Recognition

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