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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Christy Jeba Malar, A. Deva Priya, M. Naveen Kumaran, R. Sanjay Abhishek, J. Bhuvana, S. Karthick, S. Department of Information Technology Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamilnadu Coimbatore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Computer Science and Engineering SRM Institute of Science and Technology Uttar Pradesh Delhi–NCR Campus Ghaziabad India

ISBN: (纸本)9789819788354

Recent advancements in Smart Assistants (SAs) as well as home automation have captured the attention of both researchers and consumers. Virtual Assistants (VAs) that are speech-enabled are commonly referred to as smart speakers. They offer a wide range of network-based services. In some cases, they seamlessly link to smart environments, introducing innovative and efficient user interfaces. Nevertheless, these devices present their own set of challenges and limitations. The distinctive features of this technology, particularly its hand-free operation handled by voice and implementation of voice-user interface create a unique landscape. Existing technology adoption models, however, fall short in offering a comprehensive explanation for the adoption of this innovative technology. To gain deeper insights into the motivation behind approving and using in-home voice assistants, this work shifts its focus towards examining voice interactions. Utilization of speech recognition for converting speech into text enables constructing an attractive personalized assistant. This development facilitates seamless tasks such as sending emails without manual typing, conducting Google searches without opening the browser, and accomplishing various daily activities including playing music or launching a preferred IDE, all with a single voice command. To assess its performance, the system is seamlessly integrated with home automation (open-source) environment and operated continuously for some days. During this period, users are encouraged to interact with it, and the results demonstrate increased accuracy, reliability and appeal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Coot Optimization with Deep Learning-Based False Data Injection Attack Recognition

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computer Systems Science & engineering 2023年第7期46卷 255-271页

作者： T.Satyanarayana Murthy P.Udayakumar Fayadh Alenezi E.Laxmi Lydia Mohamad Khairi Ishak Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science and Engineering Aditya Engineering CollegeSurampalemAndhra PradeshIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia

The recent developments in smart cities pose major security issues for the Internet of Things(IoT)*** security issues directly result from inappropriate security management protocols and their implementation by IoT gadget ***-attackers take advantage of such gadgets’vulnerabilities through various attacks such as injection and Distributed Denial of Service(DDoS)*** this background,Intrusion Detection(ID)is the only way to identify the attacks and mitigate their *** recent advancements in Machine Learning(ML)and Deep Learning(DL)models are useful in effectively classifying *** current research paper introduces a new Coot Optimization Algorithm with a Deep Learning-based False Data Injection Attack Recognition(COADL-FDIAR)model for the IoT *** presented COADL-FDIAR technique aims to identify false data injection attacks in the IoT *** accomplish this,the COADL-FDIAR model initially preprocesses the input data and selects the features with the help of the Chi-square *** detect and classify false data injection attacks,the Stacked Long Short-Term Memory(SLSTM)model is exploited in this ***,the COA algorithm effectively adjusts the SLTSM model’s hyperparameters effectively and accomplishes a superior recognition *** proposed COADL-FDIAR model was experimentally validated using a standard dataset,and the outcomes were scrutinized under distinct *** comparative analysis results assured the superior performance of the proposed COADL-FDIAR model over other recent approaches with a maximum accuracy of 98.84%.

关键词： False data injection attack security internet of things deep learning coot optimization algorithm

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Enhanced Speaker Accent Classification: An Experimental Study of Diverse Machine Learning Methods 1

Enhanced Speaker Accent Classification: An Experimental Stud...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, R.N. Gebremariam, Selam Habtewold Sivakumar, N. Komala, G. Alla, Poorna Chandra Reddy Chauhan, Shailendrasinh Marwadi University Department of Computer Engineering Rajkot India Marwadi University Department of Information Technology Rajkot India

ISBN: (纸本)9798331529246

Speaker Accent Recognition is a supervised learning task where the objective is to predict a speaker's dialect from an audio signal. This research examines linguistic features like pronunciation and intonation to classify speakers based on their unique speech patterns. Three hundred and thirty records in our UCI dataset show six different accents: spoken English from Spanish, German, Italian, French, and both US and UK accents. The model we proposed employs Mel-Frequency Cepstral Coefficients (MFCCs) to transform raw audio into numerical data, enabling the prediction of the speaker's accent within our classification categories. Various machine learning techniques are tested such as K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and Naive Bayes, all with hyperparameter tuning. Results show that Random Forests achieved the highest accuracy at 95.62%, while Naive Bayes had the lowest at 76.09%. After tuning, SVM, Random Forests, and KNN reached accuracies of 97.31%, 95.96%, and 94.61%, respectively. The study highlights the importance of diverse algorithms and optimization techniques for effective accent recognition. © 2025 IEEE.

关键词： Random forests

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UltraSegNet:A Hybrid Deep Learning Framework for Enhanced Breast Cancer Segmentation and Classification on Ultrasound Images

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computers, Materials & Continua 2025年第5期83卷 3303-3333页

作者： Suhaila Abuowaida Hamza Abu Owida Deema Mohammed Alsekait Nawaf Alshdaifat Diaa Salama Abd Elminaam Mohammad Alshinwan Department of Computer Science Faculty of Prince Al-Hussein Bin Abdallah II for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Medical Engineering Department Faculty of EngineeringAl-Ahliyya Amman UniversityAmman19328Jordan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Faculty of Computers Science Misr International UniversityCairo11800Egypt Jadara Research Center Jadara UniversityIrbid21110Jordan

Segmenting a breast ultrasound image is still challenging due to the presence of speckle noise,dependency on the operator,and the variation of image *** paper presents the UltraSegNet architecture that addresses these challenges through three key technical innovations:This work adds three things:(1)a changed ResNet-50 backbone with sequential 3×3 convolutions to keep fine anatomical details that are needed for finding lesion boundaries;(2)a computationally efficient regional attention mechanism that works on high-resolution features without using a transformer’s extra memory;and(3)an adaptive feature fusion strategy that changes local and global featuresbasedonhowthe image isbeing *** evaluation on two distinct datasets demonstrates UltraSegNet’s superior performance:On the BUSI dataset,it obtains a precision of 0.915,a recall of 0.908,and an F1 score of *** the UDAIT dataset,it achieves robust performance across the board,with a precision of 0.901 and recall of ***,these improvements are achieved at clinically feasible computation times,taking 235 ms per image on standard GPU ***,UltraSegNet does amazingly well on difficult small lesions(less than 10 mm),achieving a detection accuracy of *** is a huge improvement over traditional methods that have a hard time with small-scale features,as standard models can only achieve 0.63–0.71 *** improvement in small lesion detection is particularly crucial for early-stage breast cancer *** from this work demonstrate that UltraSegNet can be practically deployable in clinical workflows to improve breast cancer screening accuracy.

关键词： Breast cancer ultrasound image segmentation classification deep learning

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Decoding Student Sentiments: Academic Emotion Prediction with CNN-ResNet 5

Decoding Student Sentiments: Academic Emotion Prediction wit...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Rathi, Snehal Khedkar, Vijayshri Ghule, Gauri Diwan, Niraj Atre, Kushagra Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Pune India Vishwakarma Institute of Information Technology Department of Entc Pune India

ISBN: (纸本)9798331504403

Student emotions significantly influence the learning process, yet traditional methods of monitoring emotional well-being are challenging. This research explores the potential of technology to understand and respond to student emotions in real-time. By analyzing facial expressions, body language, and physiological signals, the study aims to develop computer models capable of accurately predicting student emotions. This information can empower teachers to adapt their teaching methods to address the unique emotional needs of each student, creating a more engaging and effective learning environment. Machine learning and deep learning models, such as CNN and ResNet, are employed to analyze student data and predict emotions. The models are trained on a dataset of student facial expressions and body language to enhance accuracy and effectiveness. This research contributes to the development of innovative tools for improving student engagement and well-being in the classroom. By understanding and addressing student emotions, educators can create more personalized and supportive learning environments. © 2024 IEEE.

关键词： Adversarial machine learning

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FRCD: Future Ready Career-Duck a Machine Learning-Driven Career Guidance for Students

FRCD: Future Ready Career-Duck a Machine Learning-Driven Car...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Paritosh, Priyanshu Yadav, Vedant Gupta, Dev Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Mechatronics and Automation Engineering Bhagalpur India Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798331540821

In today's society, finding a suitable career is crucial. High school students frequently need to fully understand the notion of labor due to a lack of experience and practical knowledge in various disciplines. This renders students particularly vulnerable to external influences while deciding on a career path. As a result, students must carefully consider high-paying careers to ensure that they align with their interests and skills. Otherwise, students may end up in jobs they dislike or are not suited to. In response to this issue, we intend to develop a machine-learning framework capable of predicting career pathways. Students will receive career recommendation from our model based on their performance and potential. Students can confidently follow their chosen fields if they do well academically;if not, they can choose to change to other fields that better suit them. There are challenges in determining the metrics and improving prediction accuracy to quantify students' career paths and performance in teaching-learning. Best on the test results, the proposed model has a higher prediction accuracy. Also, this method is both reliable and portable, and it may be used to predict career paths in real time. © 2024 IEEE.

关键词： Students

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Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

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Recent Advances in computer Science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham Science Publishers.

关键词： Deep learning

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Comparative Analysis of Machine Learning Algorithms for Emotion Classification 7

Comparative Analysis of Machine Learning Algorithms for Emot...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Deshmukh, Ashish Dhage, Aniket Gadapa, Ritika Butle, Sanika Yenkikar, Anuradha Sable, Nilesh P. Vishwakarma Institute of Information Technology Department of Cse - Artificial Intelligence Pune India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331527822

In this generation, social media platforms such as Twitter, Instagram and also e-commerce websites have become an integral part of our society. Social media is used for communication and it is also a platform where the users can express their thoughts and feelings. Also, users share their reviews on e-commerce websites. To analyze what exactly is the emotion of the user our machine learning models explores the sentiment analysis techniques for predicting emotions from the given text data. It follows a series of steps, starting with importing libraries, acquiring datasets, visualization, pre-processing, and model building. The data is used to train on a variety of models, including LSTM, Naive Bayes, Random Forest, Gradient Boosting, and SVM. The performance of each model is evaluated using metrics like accuracy, precision, recall, and F1-score. Comparative analysis is conducted to determine the most effective model for emotion prediction. Furthermore, the research investigates emotion prediction for huge datasets and provides a procedure for exporting trained models for future usage. It provides insights into the performance of various machine learning algorithms in sentiment analysis, hence contributing to the understanding and interpreting emotions from textual data on various platforms. © 2024 IEEE.

关键词： Adversarial machine learning

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