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A comprehensive tool for rapid and accurate prediction of disease using DNA sequence classifier

Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13869-13885页

作者： Mathur, Garima Pandey, Anjana Goyal, Sachin Department of Computer Science and Engineering UIT RGPV Bhopal India Department of Information Technology UIT RGPV Bhopal India

In the current pandemic situation where the coronavirus is spreading very fast that can jump from one human to another. Along with this, there are millions of viruses for example Ebola, SARS, etc. that can spread as fast as the coronavirus due to the mobilization and globalization of the population and are equally deadly. Earlier identification of these viruses can prevent the outbreaks that we are facing currently as well as can help in the earlier designing of drugs. Identification of disease at a prior stage can be achieved through DNA sequence classification as DNA carries most of the genetic information about organisms. This is the reason why the classification of DNA sequences plays an important role in computational biology. This paper has presented a solution in which samples collected from NCBI are used for the classification of DNA sequences. DNA sequence classification will in turn gives the pattern of various diseases;these patterns are then compared with the samples of a newly infected person and can help in the earlier identification of disease. However, feature extraction always remains a big issue. In this paper, a machine learning-based classifier and a new technique for extracting features from DNA sequences based on a hot vector matrix have been proposed. In the hot vector representation of the DNA sequence, each pair of the word is represented using a binary matrix which represents the position of each nucleotide in the DNA sequence. The resultant matrix is then given as an input to the traditional CNN for feature extraction. The results of the proposed method have been compared with 5 well-known classifiers namely Convolution neural network (CNN), Support Vector Machines (SVM), K-Nearest Neighbor (KNN) algorithm, Decision Trees, Recurrent Neural Networks (RNN) on several parameters including precision rate and accuracy and the result shows that the proposed method gives an accuracy of 93.9%, which is highest compared to other classifiers. © 2022

关键词： Support vector machines

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Efficient Collision-Free Motion Planning in Distributed Multi-Robot Systems 17

Efficient Collision-Free Motion Planning in Distributed Mult...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Sharma, Om Chakraborty, Nilotpal India Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798331531195

In distributed multi-robot systems, ensuring collision-free motion planning is a complex challenge, especially in dynamic environments where multiple robots are operating simultaneously. Traditional path-planning algorithms are effective for individual robot navigation but struggle when dealing with the unpredictable movements of other robots in the system. To overcome these limitations, this research focuses on advanced motion planning techniques that ensure safe and efficient robot coordination. We model the overall motion planning problem into a two-dimensional grid and our objective is to minimize both the time and the energy taken by the robots to reach their destinations from their respective sources. We enhance standard path-finding algorithms such as A∗ search, along with Deep Reinforcement Learning (DRL) with suitable modifications to adapt to real-time changes. DRL allows robots to learn from their environment and make optimal decisions, improving their ability to avoid collisions and navigate efficiently in uncertain conditions. By integrating these algorithms and conducting a comparative analysis, we propose a framework that enables robots to dynamically adjust their paths, ensuring collision-free movement and optimal coordination. © 2025 IEEE.

关键词： Robot programming

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models 2

Dysphonic Voice Pattern Based Parkinson Disease Detection Us...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rhythm Khandelwal, Rajat Badlani, Prerna Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350371055

Parkinson's Disease stands as a widespread cerebral degenerative disorder impacting millions of people across the world. Timely and precise diagnosis of PD is of paramount importance, enabling early interventions and enhancing overall patient care. The presented work focuses on non-invasive detection of PD through voice analysis, targeting early intervention. Dysphonic voice patterns, indicative of PD progression, are analyzed using advanced signal processing and machine learning. Methodology involves collecting voice recordings from PD-affected individuals and healthy controls, with rigorous analysis of acoustic features. This approach minimizes patient discomfort and reduces healthcare costs, potentially revolutionizing PD care and enhancing patient quality of life. This study explored the efficacy of 4 machine learning models - Decision Trees, k-Nearest Neighbors, Support Vector Machine, and Naive Bayes- in Parkinson's Disease (PD) detection. k-Nearest Neighbors classifier demonstrates much better performance compared to the other three, achieving an accuracy of 96.61%. © 2024 IEEE.

关键词： Dysphonic Voice Pattern KNN Machine Learning Parkinson's Disease

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Robust Device-Free Mmwave Sensing with Specular Reflection Interference Mitigation

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5749-5764页

作者： Liu, Yulin Wang, Jie Gao, Qinghua Pan, Miao Fang, Yuguang Dalian Maritime University School of Information Science and Technology Dalian116026 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

Device-Free mmWave Sensing (DFWS) could sense target state by analyzing how target activities influence the surrounding mmWave signals. It has emerged as a promising sensing technology. However, when employing DFWS indoors, specular reflection interference arises due to the specular reflectors. This interference often induces ghost targets, impacting the accurate estimation of the number and position of targets, resulting in degradation in sensing performance. To tackle this issue, we delve into the generation mechanism of specular reflection interference and analyze its multi-domain characteristics. Through exploration, we discern its temporal sparsity, spatial symmetry or collinearity, and frequency correlation characteristics, and propose four metrics to measure them, accordingly. Specifically, we propose a temporal characteristic quantitative evaluation metric based on identity matching, spatial symmetry and collinearity quantitative evaluation metrics based on geometric analysis, and a frequency correlation quantitative evaluation metric based on Doppler velocity correction, respectively. Based on these metrics, we design a novel Specular Reflection Interference Mitigation (SRIM) method and develop a robust SRIM-DFWS prototype system based on a 60 GHz mmWave radar to validate our proposed method. Experimental results demonstrate that our proposed method could achieve accurate and effective mitigation of specular reflection interference in device-free target tracking. © 2002-2012 IEEE.

关键词： Radar interference

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Abnormal Activity Detection and Classification of Bus Passengers with In-Vehicle Image Sensing

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IEEE Access 2024年 12卷 23057-23065页

作者： Lin, Huei-Yung Tseng, Chun-Han National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan

As the self-driving technology is getting mature for public transportation applications, the safety concern of onboard passengers has become an important issue. It is essential to identify inappropriate or hazardous behaviors of passengers for the vehicles without human operators. In this work, we propose a technique to detect and classify the abnormal activities of passengers in a bus environment. Different from the existing human activity classification algorithms, our approach reduces the occlusion and increases the recognition rate by acquiring images from an overhead vision system. To overcome the increased complexity on feature extraction and classification, an action recognition network for top-view images are proposed by incorporating both spatial and temporal information. An image dataset, BUS-HAR, is generated for practical application scenarios with bus passengers. Experiments using real-world scene images have demonstrated the feasibility of our technique compared to existing approaches. The codes and image dataset are made available publicly at https://***/richardkuo1999/passenger-action-recognition. © 2013 IEEE.

关键词： Extraction

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Enhancing Heart Disease Prediction with GANs in Clinical Decision Support Systems 6

Enhancing Heart Disease Prediction with GANs in Clinical Dec...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Periya Nayaki, A. VishwaShree, S.V. Mithra, N. Priyadharshini, C. Thanabal, M.S. Penyameen, K. Department of Information Technology Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9798331522667

Heart disease remains a leading global cause of mortality, emphasizing the need for innovative diagnostic solutions. Traditional Clinical Decision Support Systems (CDSS) often struggle with limited datasets and imbalances, impacting prediction accuracy. This study proposes a novel approach by integrating Conditional Generative Adversarial Networks (CGANs) into a CDSS framework, enhancing its ability to generate high-quality synthetic data. The augmented dataset is utilized to train a Gradient Boosting model capable of identifying subtle patterns in patient data. This methodology significantly improves prediction precision and addresses limitations of traditional techniques. The results demonstrate the model's superiority inaccurately identifying heart disease risks, making it a valuable tool for early diagnosis and clinical decision-making. By combining advanced machine learning techniques with domain- specific innovations, this research sets a new benchmark in the application of AI for healthcare. © 2025 IEEE.

关键词： Generative adversarial networks

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A Feedback-Directed Approach to Crawl Android Apps for Increasing Code Coverage

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Journal of information science and engineering 2023年第5期39卷 1129-1153页

作者： Chen, Shu-Ling Liu, Chien-Hung Xiao, Wen-Quan Department of Industrial Management and Information Southern Taiwan University of Science and Technology Tainan700 Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106 Taiwan

To automate GUI testing for Android apps, a popular technique is to use a GUI craw-ler to systematically explore the GUIs of the apps while detecting possible app crashes. However, during GUI exploration, the crawler may get stuck and crawl some GUI states repeatedly, resulting in no increase in code coverage. This can significantly affect the ef-ficiency of the GUI crawler and thus the effectiveness of app crash detection. To relieve this problem, this paper proposes a feedback-directed approach to guide the behavior of the crawler. Specifically, the approach can assess whether the GUI crawler gets trapped based on the feedback from the crawling results, and dynamically adjust the priority of GUI states to visit in order to guide the crawler to improve code coverage. Particularly, to update the priority of GUI states, two feedback-directed strategies, CoverageDirectedStrat-egy and StateDirectedStrategy, are presented to lead the crawler to exercise more code or explore more GUI states, respectively. To evaluate the proposed approach, we have ex-tended our earlier Android crawler called ACE to support the approach and strategies. The experimental results show that both feedback-directed strategies can effectively detect whether ACE is trapped and guide ACE out of the traps, thereby improving code coverage. © 2023 Institute of information science. All rights reserved.

关键词： Graphical user interfaces

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A hybrid convolutional neural network and support vector machine classifier for Amharic character recognition

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Neural Computing and Applications 2024年第27期36卷 16839-16856页

作者： Tsegaye, Muluken Zemed Shashi, Mogalla Department of Information Technology Andhra University College of Engineering AP Visakhapatnam530003 India Department of Computer Science Andhra University College of Engineering AP Visakhapatnam530003 India

Optical character recognition is a way of converting scanned images of printed or handwritten documents into machine-encoded text, making it easier to store, browse, retrieve, and process electronic data. In this research, a Printed Amharic Characters Recognition dataset is prepared to train and test a model. Images in the dataset only contain 231 basic Amharic characters that are normalized to 32 × 32 pixels. In this work, a hybrid model of the two super classifiers is developed: the convolutional neural network (CNN) and the support vector machine (SVM). In this novel hybrid CNN-SVM model, CNN works as an automatic feature extractor from the raw images, and then, the extracted feature vectors are given as input to SVM for classification and recognition. A 99.84% accuracy was achieved on the own-prepared dataset and 95.59% accuracy on the benchmark Amharic Optical Character Recognition Database in classifying the testing dataset images. The proposed hybrid CNN-SVM model gave better results than the CNN with a fully connected layer. Moreover, the proposed model outperforms previously existing works attempted by others to recognize printed Amharic characters on the same and different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive System for Continuous Dissolved Oxygen Surveillance in Aquaculture 13

AquaticSafe Sentinel: An Edge-Based IoT-Powered Predictive S...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Taghizadeh, Seyedreza Fardarani, Zahra Nooraninejad Shiraz University Engineering and Information Technology Department of Computer Science Shiraz Iran

ISBN: (纸本)9798350305463

Aquaculture stands as a flourishing industry, serving as a significant financial resource for many. However, it encounters various challenges such as environmental degradation and disease outbreaks. Consequently, maintaining water quality in aquaculture systems becomes imperative. Dissolved oxygen (DO), a key indicator of oxygen availability for fish respiration, emerges as a crucial measure of water quality. In this research, we propose AquaticSafe Sentinel, an innovative IoT-backed predictive system designed for edge-based continuous monitoring of DO and real-time alerting in aquaculture. Our two-phase framework utilizes both edge computing and cloud computing to swiftly and reliably predict DO levels based on environmental and biological characteristics. Phase one involves making ensemble forecasts of DO levels at the cloud layer using a combination of three machine learning approaches, namely Linear Regression, Random Forest, and AdaBoost. Then phase two involves employing the results of Linear Regression at the edge of the network to make real-time decisions about DO levels, and triggering alerts if necessary. Subsequently, the system awaits a more refined conclusion from the combination technique at the cloud layer. We conduct a comparative analysis with various baseline methods, demonstrating that our framework surpasses them in terms of prediction accuracy and computing efficiency. Additionally, we illustrate how our framework effectively minimizes false alerts, achieving a balance between speed and accuracy to deliver timely solutions. © 2024 IEEE.

关键词： Edge computing

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