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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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MarineLens: Empowering Conservation through AI-Powered Fish Species Detection and Insights

MarineLens: Empowering Conservation through AI-Powered Fish ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sorna Shanthi, D. Vijay, K. Bhuvaneswaran, B. Praveen, N. Priyan, B. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390049

MarineLens is a groundbreaking conservation tool that leverages the power of artificial intelligence to revolutionize fish species detection and insights in marine environments. Using advanced machine learning algorithms, MarineLens is able to accurately identify and track different fish species, providing researchers and conservationists with valuable data on population dynamics, behavior patterns, and habitat usage. By harnessing the capabilities of AI, this technology offers a cost-effective and efficient solution for monitoring marine life and assessing the impact of human activities on underwater ecosystems. MarineLens enhances the ability to make data-driven decisions and implement targeted conservation strategies to protect vulnerable species and preserve biodiversity. With its user-friendly interface and customizable features, MarineLens empowers users to generate actionable insights and visualize complex data in a comprehensible manner, facilitating collaboration and knowledge sharing within the conservation community. Enabling a proactive approach to marine conservation, MarineLens is a game-changer in safeguarding our oceans for future generations. © 2024 IEEE.

关键词： Scalability Oceans Marine vegetation Fish Biodiversity Environmental management Sustainable development Protection Marine animals Monitoring

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A Novel AI-Assisted e-Consult Platform Integrating Deep learning for Enhanced Healthcare Access and Diagnostic Precision

A Novel AI-Assisted e-Consult Platform Integrating Deep Lear...

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2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Vijayasekaran, G. Rajasree, R.S. Alam, Parvez Suryavanshi, Abhishek Ranjanagi, Vinayak Mulla, Umar Farooq New Horizon College of Engineering Dept of Computer Science and Engineering Bengaluru India Learning New Horizon College of Engineering Dept of Artificial Intelligence and Machine Bengaluru India

ISBN: (纸本)9798331509828

This study introduces the 'AI-Assisted E-Consult' initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult platform, serving as a digital interface that links patients, healthcare provider, and medical facilities. Utilizing state-of-the-art technologies such as AI and machine learning, the platform streamlines predictive health insights and appointment scheduling, thereby improving access to quality medical care. The platform employs Natural Language Processing (NLP) via the NLTK library and uses a Recurrent Neural Network (RNN) architecture trained on medical conversation datasets to power an AI chatbot. This chatbot analyzes symptoms and predicts potential health conditions, offering users preliminary insights into their health status. Furthermore, the system incorporates Haversine distance calculations to measure the geographical proximity between patients and doctors, enhancing logistical planning for consultations. To evaluate the platform's accuracy and performance, metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) will be used, ensuring precise predictions and operational effectiveness. The AI- Powered E-Consult platform is designed to eliminate healthcare barriers and maximize resource efficiency. Offering scalable, adaptable solutions tailored to the needs of contemporary healthcare, this platform represents a significant advancement toward a more accessible, efficient, and time-saving healthcare model, particularly for underserved populations. © 2025 IEEE.

关键词： Recurrent neural networks

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A Survey on Graph Neural Networks and its Applications in Various Domains

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SN computer science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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Kontho: An AI-Driven Smart Glove for Enhanced Sign Language Communication 2nd

Kontho: An AI-Driven Smart Glove for Enhanced Sign Language...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Dasgupta, Dyuti Datta, Soumyajit Pramanik, Sagnik Dey, Rohit Kumar Nandigrami, Jeet Ghosh, Debaditya Department of Artificial Intelligence and Machine Learning Heritage Institute of Technology Kolkata India Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India

ISBN: (纸本)9783031845420

In India, over 63 million individuals are affected by hearing loss, yet less than 350 certified sign language interpreters are available, thus leading to communication barriers and significant social and economic exclusion. This paper introduces Kontho, an ergonomic, AI-driven, multi-sensor glove integrated with a mobile app for real-time sign language communication via American Sign Language (ASL). Utilizing flex sensors, accelerometers, gyroscopes and EMG sensors, Kontho captures gestures and muscle activity which is processed through a combination of ensemble models and Large Language Models (LLMs) to enhance communication by predicting and completing incomplete gestures. Evaluated on a dataset of 23 ASL gestures at over 3867 data points with an accuracy of 99.61%, Kontho offers a scalable solution to foster social inclusion and address the economic isolation of hearing and speech-impaired individuals, enabling their active participation in the society. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signaling

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Optimizing AUV Positioning and Data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 IEEE.

关键词： Sensor nodes

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Precision Land Use and Land Cover Mapping with a High-Accuracy Time Series LSTM Model

Precision Land Use and Land Cover Mapping with a High-Accura...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

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Brain Tumor Detection using YOLO v7 and YOLO v8

Brain Tumor Detection using YOLO v7 and YOLO v8

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Vaghela, Rohan Sarda, Jigar Thakkar, Amit Vaishnani, Darsh Khokhariya, Avani Mehta, Pooja Charotar University of Science & Technology Department of Computer Science & Engineering Anand India Charotar University of Science & Technology Department of Artificial Intelligence & Machine Learning Anand India

ISBN: (纸本)9798331533311

The architecture of the brain is incredibly complex, with multiple areas managing various parts of the nervous system. An adult's health is greatly at risk from brain tumors because of their ability to severely impair organ function or possibly cause death due to their rapid growth of abnormal brain cells. Brain tumors can develop in any area of the skull or brain, including the protective layer around the brain and many other areas. Since these tumors can have a wide range of shapes, sizes, and locations, magnetic resonance imaging (MRI) is an essential tool for locating malignant tumors. MRIs are examined manually in order to diagnose brain tumors. However, manually identifying brain tumors is a challenging and time-consuming task that may result in errors. As a result, computer-aided brain tumor detection has been considered a crucial area of study in biomedical engineering. However, because tumor sizes and shapes vary enormously throughout subjects, developing a reliable brain tumor detector remains difficult. Thus, for the purpose of accurately identifying brain tumors in MRI scans, two popular deep learning detection algorithms - YOLO v7 and YOLO v8 - were tested and compared in this study. From the results, YOLO v8 model has achieved highest validation accuracy of 96.50% with average precision and recall value of 95.9% and 93.3% respectively, while YOLO v7 has validation accuracy of 87.6% with average precision and recall value of 81.9% and 85.4% respectively. © 2024 IEEE.

关键词： Deep learning

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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ClinicCare Manager: An Efficient Electronic Health Record (EHR) and Healthcare Management System Using Spring Boot and React 5

ClinicCare Manager: An Efficient Electronic Health Record (E...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Bindu, S. Ashok, K. Krishnamurthy, Keerthana Chethan, C. Jatinkrishna, Gajula Srinivas Gagan, V. New Horizon College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India New Horizon College of Engineering Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518097

Healthcare institutions often face challenges in managing patient data, optimizing clinic workflows, and ensuring timely reporting. Smaller clinics, in particular, may lack the resources to streamline services for both patients and healthcare providers. This work introduces a platform that automates patient data analysis and report generation to address these needs. Built using React, Spring Boot, and PostgreSQL, the application provides efficient, end-to-end service with robust data handling and fast processing. Performance metrics show efficient resource utilization, with CPU usage rarely exceeding 3 % and heap memory remaining well within the 2048 MB limit. Compared to other health care applications, this platform offers greater scalability and responsiveness. Additionally, the framework supports secure authentication and role-based access, making it a reliable and scalable solution that meets the expanding demands of health care environments. © 2024 IEEE.

关键词： Electronic health record

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