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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Sarker, Iftekhar Alam Khan, Mizanur Rahman Riad Prova, Sadia Islam Khan, Mohshiu Islam Morshed, Md. Adnan Reza, Ahmed Wasif Arefin, Mohammad Shamsul East West University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Clinical microbiology receives significant advantages from automated bacterial species identification and classification using microscopic images. Bacterial classification from microscopy pictures takes a long time and is difficult even for expert biologists;thus, in this paper, an automated deep learning-based classification strategy is provided. The purpose of this work is to offer a feasibility study using polyculture images to reduce diagnostic time and expense. The ResNet50 pre-trained architecture method was utilized to divide digital images into 33 categories. ResNet50 has remarkable feature extraction abilities through precise data preprocessing and model training. Enhancing robustness augmentation techniques enables accurate classification even with unseen data. This method obtained an average classification accuracy of 94.91% and can be used for any bacterial classification. This model is ready to use in the clinical microbiology section. © 2024 IEEE.

关键词： Bacteria

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Enhanced Deep SORT with Class Filtering for ID Switches Minimization in Visual Object Tracking 21

Enhanced Deep SORT with Class Filtering for ID Switches Mini...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Maung, Khin Ohnmar Myint, Theingi Mandalay Technological University Department of Computer Engineering and Information Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

A new approach for tracking the visual objects is proposed to enhance Deep SORT with class filtering in this system. Currently, visual object tracking systems are most popular in real life. But there are still challenging tasks such as illumination change, occlusion and reappearing on camera's view, and the tracked objects' ID switching. Moreover, in the tracking process, false positive tracks are changed the tracking object's ID, and different detection class and tracking class are also problems for ID switching cases. The main emphasis of this paper is to minimize IDs' changes of objects. In this paper, low-confidence, and proposed class filters are implemented and tested on own dataset to enhance Deep SORT. In the experimental results, the changes in tracking specified objects' ID of the two filtering can minimize by 10% and 67% respectively rather than that of the Deep SORT. Therefore, we found that a new approach can significantly minimize the IDs' changes of objects. © 2024 IEEE.

关键词： class filter Deep SORT ID switching

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Exploration of Semantic Priming Studies in Native Language of Children:Study on Bengali L1 and English L2 Bilingual Children

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SN computer science 2025年第2期6卷 1-18页

作者： Banerjee, Jaya Nithin, Kota Sai Sai, Uppalapati Hari Krishna Chakraborty, Baisakhi Basu, Anupam Department of Computer Science & Engineering National Institute of Technology West Bengal Durgapur 713209 India National Institute of Technology West Bengal Durgapur India Department of Computer Science & Engineering Indian Institute of Technology (IIT) Kharagpur 721302 India

Word Priming is an event in which prior subjection to a particular word impacts the reply of the individual to the upcoming correlated word. The response time is inferred to be less in identifying the target word preceded by an associated word compared to the word in seclusion or isolation as per the experiments undertaken on an Indian language where research incorporates Bengali-speaking children. Bengali is an important language of consideration on national and international platforms. A set of twenty-one school-going children with ages in the range of 7 to 10 years are subjected to a word priming assessment. Analysis established a positive impact of word prime in the reaction of a set of children aged seven to ten to certain listed words Due to the presence of word prime, the average error measure of the lower progressing answerers reduced by a significant 19.8%. With reference to the Bengali script, the average error measure is 10 percent. With the involvement of the word prime, the mean error measure is 1.6 percent. This paper proposes first of its kind of research studies and analysis with regard to the positive effect of word priming on the response time of target word identification on Bengali speaking bi-lingual dyslexic children to yield positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bengali Children English Learning impairment Priming Psycholinguistics Second language Word identification Word priming

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InteractNet: Improved Drug-Drug Interaction Prediction in Pharmacology Using Deep Neural Networks 18th

InteractNet: Improved Drug-Drug Interaction Prediction in P...

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18th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2024

作者： Chaurasia, Anushka Kumar, Deepak Yogita, Yogita Department of Computer Science and Engineering National Institute of Technology Meghalaya Shillong India Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India

ISBN: (纸本)9783031878725

In the context of modern medicine, where polypharmacy is prevalent, particularly among older adults, the potential for hazardous drug-drug interactions (DDIs) is a significant concern. These interactions can lead to severe adverse reactions, with consequences ranging from debilitating health complications to fatality. Previously, distinct machine learning and deep learning algorithms were used to predict DDIs using gold-standard datasets. These methods demonstrate remarkable accuracy but lack precision due to a high false positive rate. Therefore, there is a crucial need to enhance the models’ ability to reduce the false positive rate. This paper introduces a deep learning-based framework named InteractNet to enhance precision in DDI predictions. This framework employs 1D convolutional neural networks with streamlined parameterization in the convolutional layer for multi-scale feature extraction, bidirectional long short-term memory to capture drug feature interactions, and an attention mechanism to concentrate on key data features for precise predictions. The efficacy of InteractNet is thoroughly examined through five performance metrics, namely accuracy, precision, true positive rate, harmonic mean, and ROC-AUC score. Results indicate that InteractNet achieves the highest testing accuracy of 94.29 ± .0088, a precision of 94.23 ± .0023, a true positive rate of 94.49 ± .0037, a harmonic mean of 94.21 ± .0029, and a ROC-AUC score of 96.24 ± .0032. These outcomes surpass those of existing state-of-the-art methods, underscoring the profound potential of deep learning to enhance medical safety. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Drug interactions

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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6th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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Deep Learning Approach for Analysis and Characterization of COVID-19

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computers, Materials & Continua 2022年第1期70卷 451-468页

作者： Indrajeet Kumar Sultan S.Alshamrani Abhishek Kumar Jyoti Rawat Kamred Udham Singh Mamoon Rashid Ahmed Saeed AlGhamdi School of Computing Graphic Era Hill UniversityDehradun(UK)India Department of Information Technology College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Computer Science&IT JAIN(to be Deemed University)BangaloreIndia School of Computing Dehradun Institute of TechnologyUttarakhandIndia Department of Computer Engineering Faculty of Science and TechnologyVishwakarma UniversityPuneIndia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more *** a recent pandemic,laboratories perform diagnostics manually,which requires a lot of time and expertise of the laboratorial technicians to yield accurate ***,the cost of kits is high,and well-equipped labs are needed to perform this ***,other means of diagnosis is highly *** is one of the existing methods that finds its use in the diagnosis of *** radiography observes change in Computed Tomography(CT)chest images of patients,developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based *** proposed work suggests an Artificial Intelligence(AI)based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning *** proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 *** proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases,while 397 belong to negative cases of *** experiment resulted in an accuracy of 98.4%,sensitivity of 98.5%,specificity of 98.3%,precision of 97.1%,and F1-score of 97.8%.The additional parameters of classification error,mean absolute error(MAE),root-mean-square error(RMSE),and Matthew’s correlation coefficient(MCC)are used to evaluate our proposed *** obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases.

关键词： Coronavirus covid-19 respiratory infection computed tomography deep neural network

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Advanced Machine Learning Approach for Cyber Attack Detection in Cloud Computing

Advanced Machine Learning Approach for Cyber Attack Detectio...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Yadav, Shashi Bhushan Singh Shelke, Nitin Arvind Singh, Jagendra Yasmeen Pandey, Deependra Himanshi Bbd Northern India Institute of Technology Department Computer Science & Engineering Lucknow India School of Computer Science Engineering & Technology Bennett University Greater Noida India Cvr College of Engineering Department of CSE-DS Hyderabad India Amity School of Engineering and Technology Amity University Department of Ece Lucknow India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India

ISBN: (纸本)9798331527495

Cloud computing has become the most important part of modern IT infrastructure, which is scalable and flexible. However, with such wide-scale adoption, new vulnerabilities have been introduced into the system, making it a favorite target for cyber-attacks, especially Distributed Denial-of-Service (DDoS) and Man-in-the-Cloud (MitC) attacks. Security measures in cloud environments are more than ever required to be effective. This study introduces a state-of-the-art approach using machine learning techniques to identify and prevent such attacks;the four machine learning algorithms that have been used here are Support Vector Machine, Artificial Neural Network, Recurrent Neural Network, and k-Nearest Neighbors. A dataset of 3,200 simulated records has been used for the purpose of training and testing;70% of data were used for training purposes, and 30% of data were used for testing purposes. The models were rated based on precision, running time, and memory usage. The results highlighted that SVM was the strongest model and had an accuracy of 97.86%, while ANN was about 93.4% followed by RNN which was about 89.45% and KNN at 86.7%. This would mean that among the above models, the SVM is the most efficient with respect to real-time attack detection as it is in high precision and has efficiency in resource use. The proposed approach can be very well applied to real-time cloud security systems, offering a robust solution in identifying and mitigating cyber threats in dynamic cloud environments, ensuring enhanced protection against evolving attack vectors. © 2025 IEEE.

关键词： Recurrent neural networks

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ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with Uncertainty Data 8

ARIMA-LP: A Hybrid Model for Air Pollution Forecasting with ...

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8th IEEE International Conference on Software engineering and computer Systems, ICSECS 2023

作者： Che Lah, Muhammad Shukri Arbaiy, Nureize Lin, Pei-Chun Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Malaysia Feng Chia University Department of Information Engineering and Computer Science Taiwan

ISBN: (纸本)9798350310931

Public health is seriously threatened by air pollution. Systems for early warning are crucial for preventing its negative impacts on humans. But predicting air quality is difficult because it needs precise data from time series gathered at air monitoring sites. Due to things like measurement mistakes, this data may be ambiguous. Using fuzzy symmetry triangular fuzzy numbers, we suggest a strategy in this study for preparing data that contains uncertain information. Linear programming is utilized to obtain the midpoint value for defuzzification. The fuzzy pre-processed data is then used to build a predictive model using ARIMA. Our findings imply that the linear program can greatly lower prediction errors, resulting in more precise predictions. © 2023 IEEE.

关键词： Linear programming

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Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification

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computers, Materials & Continua 2022年第9期72卷 5041-5058页

作者： Maqsood Ahmad Noorhaniza Wahid Rahayu A Hamid Saima Sadiq Arif Mehmood Gyu Sang Choi Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia86400 Parit RajaBatu PahatJohorMalaysia Department of Computer Science Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan 38542Korea

Mental health signifies the emotional,social,and psychological well-being of a *** also affects the way of thinking,feeling,and situation handling of a *** mental health helps in working with full potential in all stages of life from childhood to adulthood therefore it is of significant importance to find out the onset of the mental disease in order to maintain balance in *** health problems are rising globally and constituting a burden on healthcare *** diagnosis can help the professionals in the treatment that may lead to complications if they remain *** machine learning models are highly prevalent for medical data analysis,disease diagnosis,and psychiatric *** research addresses the challenge of detecting six major psychological disorders,namely,Anxiety,Bipolar Disorder,Conversion Disorder,Depression,Mental Retardation and *** challenges are mined by applying decision level fusion of supervised machine learning algorithms.A dataset was collected from a clinical psychologist consisting of 1771 observations that we used for training and testing the ***,to reduce the impact of a conflicting decision,a voting scheme Shrewd Probing Prediction Model(SPPM)is introduced to get output from ensemble model of Random Forest and Gradient Boosting Machine(RF+GBM).This research provides an intuitive solution for mental disorder analysis among different target class labels or groups.A framework is proposed for determining the mental health problem of patients using observations of medical *** framework consists of an ensemble model based on RF and GBM with a novel SPPM *** proposed decision level fusion approach by combining RF+GBM with SPPM-MIN significantly improves the performance in terms of Accuracy,Precision,Recall,and F1-score with 71\%,73\%,71\%and 71\%*** framework seems suitable in the case of huge and more diverse multiclass ***,three

关键词： Mental health diagnosis machine learning depression shrewd probing diagnostic approach

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Fake News Detection: Exploring the Efficiency of Soft and Hard Voting Ensemble 4

Fake News Detection: Exploring the Efficiency of Soft and Ha...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rahman, Arifur Zaman, Sakib Parvej, Shahriar Shill, Pintu Chandra Salim, Md. Shahidul Das, Dola Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna9203 Bangladesh

Fake news dissemination is a critical problem, jeopardizing the reliability of information and eroding public confidence. Machine learning provides a solution by distinguishing between real and misleading news. In this study, we utilized various classifiers such as KNN, Logistic Regression, Decision Tree, LightGBM, XGBoost, Gradient Boosting, Random Forest, and Naive Bayes on an open-source dataset. Tf-idf (Term frequency-inverse document frequency) and Count vectorization were used for text preprocessing, and soft and hard voting ensembles were applied to the best three and five models to boost performance. Additionally, we employed BERT (Bidirectional Encoder Representations from Transformers) and CNN (Convolutional Neural Network) models with various optimizers (adam, rmsprop, SGD, adadelta, and adagrad) to further improve classification. The BERT model, optimized with adam, achieved the highest accuracy of 99.93%, with perfect precision (100%) and high recall (99.84%). Furthermore, using Count vectorization for feature extraction, the soft voting ensemble of the top five models (LGBM, XGB, RF, DT, LR) achieved peak performance among all ensemble models, achieving 99.87% accuracy, with precision, recall, f1-score, MAE, MSE, RMSE, RAE, and RRSE of 99.83%, 99.88%, 99.86%, 0.13%, 0.13%, 3.66%, 0.27%, and 7.80%, respectively. The exhaustive experimentation confirms the applicability and efficiency of the recommended models in identifying fake news. © 2025 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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