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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Performance Analysis of Hybrid Cryptographic Algorithms in Serverless Platforms 1

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3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Raj, Vinay Suresh, Satharla Phridviraj, M.S.B. Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

Serverless computing has become the buzzword in IT and it has been widely accepted because of its diverse cloud computing benefits. One of the major drawbacks of serverless computing is the security of the data which is handed over to the third parties. To ensure the security of the data and secure private communication between the services, hybrid cryptographic algorithms play a predominant role by combining the best security features of symmetric and asymmetric encryption techniques. In this work, two hybrid cryptographic algorithms are proposed namely 2-tier and 3-tier models and the performance of the two algorithms are analyzed in terms of encryption and decryption time along with the file size. The obtained results are also compared with an existing hybrid cryptographic model and it is observed from the results that the proposed hybrid model outperforms the existing model on both the 2-tier and 3-tier models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's institute of computer applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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Intelligent Analysis and Identification of Palm Gestures using ML Algorithms

Intelligent Analysis and Identification of Palm Gestures usi...

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2024 International Conference on computer, Electrical and Communication engineering, ICCECE 2024

作者： Roy, Debraj Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar De, Utpal Chandra Tripathy, Abinash School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798350386479

The area of human-computer interaction (HCI) has made significant strides in recent years, thanks in large part to the development of innovative technologies that aim to improve the way in which people and machines work together. One of these areas is the use of hand gestures to control gadgets and apps, which has the potential to create a form of interaction that is more intuitive and natural. The introduction of a palm gesture detection system that is both dependable and effective has the potential to completely alter the ways in which we interact with various electronic gadgets and software programmes. The objective is to develop a palm gesture recognition system that is capable of precise detection and classification of hand gestures in real time. The computer vision methods is applied to collect and interpret the hand movements. Following this, machine learning algorithms will be used to train the system on a huge dataset consisting of hand gestures. The system will be able to identify the hand gestures in real time and will categorise them according to predetermined categories. The experimental results are compared against other gesture recognition systems to study the accuracy of the system. © 2024 IEEE.

关键词： Support vector machines

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Colossal pattern extraction using optimised length constraints based on differential evolutionary arithmetic optimisation algorithm

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International Journal of Information and Communication Technology 2024年第3期24卷 289-304页

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjunarao Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Science and Engineering Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Department of Computer Applications Annamacharya Institute of Technology and Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

Extracting large amounts of information and knowledge from a large database is a trivial task. Existing bulk item mining algorithms for an extensive database are systematic and mathematically expensive and cannot be used for large-scale mining with interruptions. In this paper, the problem of mining the colossal patterns (CPs) is solved by using optimised length constraints (LCs). First, we describe the minimum LC and maximum LC problems and connect them to the optimal LC by identifying the optimal threshold values. Here, the differential evolutionary arithmetic optimisation algorithm (DAOA) is used to find the optimal threshold values of the constraints and extract the colossal patterns. The effectiveness of the proposed algorithm is proven by various experiments using different biological datasets. © 2024 Inderscience Enterprises Ltd.

关键词： Database systems

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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Detection of Eye Disease Using Deep Learning Algorithms 4

Detection of Eye Disease Using Deep Learning Algorithms

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Patel, Omkumar J. Verma, Kshitij Vyas, Chirayu Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Applications Kiit Deemed to be University Bhubaneswar India

ISBN: (纸本)9798331540364

The human eye is a complex organ that can perceive a wide variety of visual inputs. However, its functioning can be severely impaired by diseases such as cataract and diabetic retinopathy which have the potential to cause permanent blindness which not only disrupts the productivity and mobility of patients but also causes a socio-economic impact that decreases the quality of life. This paper reviews the recent advancements in deep learning for diagnosing retinal diseases such as diabetic retinopathy (DR), cataract and glaucoma. The paper presents an extensive comparative analysis of 4 definite deep learning models - Vision Transformers (ViT), ResNet, GoogLeNet and Convolutional Neural Network (CNN). The results from the conducted experiments show that ViT has the highest accuracy followed by CNN. The outcomes highlights the potential of deep learning models in revolutionizing eye disease detection technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Advance Chess Engine: an use of ML Approach 3

Advance Chess Engine: an use of ML Approach

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Srivastava, Kushagra Pandey, Trilok Nath Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Mishra, Manoj Ranjan De, Utpal Chandra Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350381931

Several researchers have sought to create a software that can learn to play cognitive games with little or no prior understanding of the game's rules. A typical chess playing machine investigates all of the possible moves from a chessboard arrangement before deciding on the next best move. The Deep Blue chess engine's brute-force searching strategy has had a significant influence on artificial intelligence, although it has been discovered to be resource intensive. Using the evolutionary and adaptive computing strategy on learning from human grandmasters, this study proposes a relatively easy and fast approach to developing an intelligent chess engine that can aid and hint at probable moves inside the game. Chess-playing machines may now easily surpass human talent thanks to the introduction of Machine Learning, Deep Learning and Artificial Intelligence. The game of chess is examined through the prism of these algorithms in this study. The comparison of the model performance with the existing ones has been performed to show the efficacy of the model. © 2024 IEEE.

关键词： Engines

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