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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Begum, Sheik Ariffa Nammalwar, Subbulakshmi Vijayalakshmi, Jeyapal Vallabha, Manju Kalasalingam Academy of Research and Education Computer Science and Engineering Krishnankoil India Vaigai College of Engineering Computer Science and Engineering Madurai India

ISBN: (纸本)9798350369083

In contemporary organizational landscapes, the strategic retention of employees stands as a paramount concern for sustained success. To address this challenge, this research endeavors to harness the power of predictive analytics, employing a multifaceted approach integrating ensemble learning and deep learning methodologies. The preprocessing phase employs Min-Max Scaler to normalize the feature space, enhancing the efficacy of subsequent algorithms. Additionally, Synthetic Minority Over-sampling Technique (SMOTE) is employed to mitigate class imbalances, ensuring robustness in predictive modeling. Ensemble methods such as CatBoost, AdaBoost, Light GBM, and Gradient Boosting Machine (GBM) are leveraged to capture complex interactions and nonlinear relationships within employee data. Furthermore, Long Short-Term Memory (LSTM), a variant of recurrent neural networks, is utilized to uncover temporal dependencies and intricate patterns inherent in longitudinal employee records Ultimately, this endeavor contributes to the advancement of HR analytics, empowering organizations to preemptively address attrition risks and foster a resilient and engaged workforce. © 2024 IEEE.

关键词： Predictive analytics

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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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Predictive Modeling of Sedentary Behavior Patterns in Adults Using Stacked LSTM

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Journal of Harbin Institute of Technology(New Series) 2024年第4期31卷 74-84页

作者： P Sanju Department of Computer Science and Engineering University College of Engineering TindivanamTamil Nadu 604001India

A new health concern in recent periods has seen the evolution of uncertain sedentary *** sedentary for extended durations is regarded as a notable hazard across various adult age brackets,especially the excessive dependence on automobiles for *** the active period,monitoring seating habits has been made easier by ***,there exists a disagreement among professionals regarding the most suitable quantifiable criteria for encompassing the comprehensive data on sedentary behavior throughout the *** to variations in measurement methodologies,data analysis approaches,and the lack of essential outcome indicators such as the total sedentary duration,the assessment of sedentary patterns in numerous research investigations was considered *** research suggested fleeting granularity distinguish occurrences of regular human *** units(essential cells) acquire multivariate transitory *** Behavior Patterns(FBPs) can be identified with a estimation of timeframe using our proposed scalable algorithms that employ collected widespread multivariate data(fleeting granularity).The research outcome,supported by rigorous analyses on two validated datasets,mark a significant *** the final stages of the study,a stacked Long Short-Term Memory(LSTM) model was utilized to replicate and forecast repetitive sedentary behavior patterns,leveraging data from the preceding six-hour window blocks of sedentary *** model effectively replicated state traits,previous action sequences,and duration,attaining an impressive 99% accuracy level as assessed through RMSE,MSE,MAPE,and r-correlation metrics.

关键词： health care sedentary behavior LSTM

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An Efficient Multilingual Text Classification using IndicCorp dataset 5

An Efficient Multilingual Text Classification using IndicCor...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Madanbhavi, Lalitha Desai, Padmashree Sirur, Neha Dhirendra Deshpande, Ananya Hiremath, Risheek V. Patil, Chetan M. Kle Technological University School of Computer Science and Engineering Hubballi India Rv College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

Language detection is a crucial preprocessing step in natural language processing (NLP) tasks, especially in a multilingual environment. This paper presents a language detection system utilizing the Naive Bayes classifier, applied to the IndicCorp dataset, which includes a diverse range of languages prevalent in the Indian subcontinent. The IndicCorp dataset provides a rich source of textual data across multiple Indian languages, enabling the development of robust and accurate language models. Our system leverages the simplicity and effectiveness of the Naive Bayes algorithm, known for its high efficiency in text classification tasks. The proposed approach involves pre processing the dataset, extracting features using the CountVectorizer, and training the Naive Bayes classifier to identify the language of a given text. Experimental results demonstrate the system's capability to achieve an accuracy of 73.37% in language detection across various Indian languages within the IndicCorp dataset. This performance underscores the system's effectiveness in classifying languages like Hindi, Bengali, Tamil, and others, highlighting its potential for broader NLP applications and multilingual content processing. The necessity for multilingual classification arises from the need to support diverse linguistic communities and facilitate seamless communication. Accurate language detection enables various downstream applications such as machine translation, sentiment analysis, and information retrieval, enhancing user experience and accessibility. This study emphasizes the practical application of Naive Bayes for language detection and underscores the value of the IndicCorp dataset in developing language processing tools tailored to India's linguistic landscape. The achieved accuracy demonstrates our approach's robustness in handling diverse linguistic characteristics and showcases its potential impact on communication and information retrieval in multilingual contexts. © 2024 IEEE.

关键词： Data assimilation

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech Technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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An Efficient Crypto Based Ransomware Prevention Method in Android Platform 3

An Efficient Crypto Based Ransomware Prevention Method in An...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Kulshreshtha, Vyom Motwani, Deepak Sharma, Pankaj Amity University Computer Science & Engineering Madhya Pradesh Gwalior India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350384598

A relatively new kind of intrusion attack called ransomware aims to demand a ransom from its target. Ransomware attacks are carried out by cybercriminals to financially affect victims by damaging their gadgets. Due to Android's widespread use worldwide, assaults on smartphones using this operating system are growing exponentially. While there are many different kinds of ransomware attacks, this article mainly concentrates on crypto-ransomware as, once the victim's files are encrypted, it renders data irretrievable. We provided a thorough examination based on machine learning crypto-ransomware detection techniques in this study. This detection must be successful to stop the attack from achieving its objective. In this work, we provide a classification scheme for the security level of crypto-ransomware for detection as well as prevention. This framework includes data collection, preprocessing, feature extraction and selection, or classification, and the application of multiple machine learning techniques. We assessed the accuracy, recall, F1-score, and precision scores using a single Android ransomware dataset. The comparison findings show that the Support Vector Machines (SVM) classifier performs better than competing approaches across all performance metrics, with a 99.5% accuracy rate in detecting crypto Android ransomware. Unlike most previous studies that provide an analysis of crypto-ransomware, this is a thorough assessment that focuses on detection techniques. © 2024 IEEE.

关键词： Android malware

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Deep Learning Techniques for a Comparative Study of Crop Disease Detection 5th

Deep Learning Techniques for a Comparative Study of Crop Dis...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Soumya Prasad, S. Sampath Kumar, L. Mallem, Sai Nirupam Gutta, Hemanth Ahmed, Rafeeq Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India Computer Science and Engineering Government Engineering College West Champaran Bettiah India

ISBN: (纸本)9789819761050

Agriculture industry can be significantly affected by pests. Deep learning techniques are very useful for detecting various diseases in crops. A dataset consisting of 1530 images was divided into the training set, testing set, and validation set. It consists of labels such as "Healthy", "Powdery," and "Rusty." These labels reflect the conditions of a plant and are used to train the model. The trained models using Deep Convolutional Neural Networks successfully find the relevant crop diseases with a 95% accuracy rate. The early detection of diseases is useful to curb the diseases in the budding stage to avoid the rampant onset of plant diseases which prove fatal to the agricultural field. By implementing this strategy, crop loss can be reduced significantly. In this paper, a detailed explanation of various CNN architectures such as Xception, VGG16, Resnet50, DenseNet, efficientNet, and InceptionV3 is outlined. Each model is trained with training data to predict a plant’s disease. The model’s performance is highly dependent on extracting features from the data. The result analysis and visualizations of obtained results are presented in the analysis section of the paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Voice Controlled Smart Home System 2

Voice Controlled Smart Home System

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Manjula, K. Dhananjaya, V. Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India Computer Science & Engineering Impact College of Engineering Bangalore India

ISBN: (纸本)9798350372892

The Internet of Things (IoT) is a significant technological advancement that uses the internet to enable seamless communication between various devices, allowing people and objects to connect anytime and from anywhere. In this research, we present an intelligent smart automation model that can remotely operate household appliances and water the garden plants from anywhere in the world. 'Home automation' is a quickly emerging technology that minimizes the work that humans must perform. Certain home automation systems are offered to people seeking luxurious, advanced home automation systems. This method is safe because it works best for the elderly and disabled people. However, certain people could find it more challenging to use certain systems because of their special demands. The system's primary features are voice recognition and intelligent interaction. Our main objective is to develop an advanced home automation system that has combined Alexa with the ESP 8266 module to improve security, reduce power consumption and minimize user effort. The model includes two essential sensors, a soil moisture sensor, a temperature and humidity sensor. All hardware components in this model are cost-effective, readily available, and easily replaceable. © 2024 IEEE.

关键词： Soil moisture

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Well Bot—A Reinforcement Learning and Sentiment Analysis-Based Chatbot Implementation for Mental Well-Being 6th

Well Bot—A Reinforcement Learning and Sentiment Analysis-Ba...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Sohara Banu, A.R. Nagaveni, V. Gowda, Naveen Chandra Yerriswamy, T. Gururaj, M. Varshitha, N. School of Computer Science and Engineering REVA University Bengaluru India Department of Computer Science and Engineering AMC College of Engineering Bengaluru India

ISBN: (纸本)9789819986279

As the world becomes more and more competitive, the number of people experiencing stress, anxiety, and other mental health problems is rapidly rising. In today’s society, stress and pressure are affecting everyone, including school-going children and elderly people. We believe that an individual’s mental health is equally important as their physical health. But unfortunately, there are not enough resources for mental health care or adequate facilities in Indian society;thus, individuals struggle on their own, without assistance or support. Therefore, we suggest a solution for designing a chatbot that will serve as a communication channel for users. The users can let out their feelings and thus get relieved of the stress and/or anxiety that is clogged up. The chatbot application will send pop-up notifications to the user based on inputs of the user’s mood from Google Health installed on the mobile. The sentiment analysis module will generate appropriate notifications and converse with the user once they begin chatting with the chatbot. The chatbot uses Google’s Dialogflow services, including natural language processing and reinforcement learning, to generate responses. This will benefit people who are depressed and are afraid to express their feelings out loud or fear being judged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Digitalization of Objects using Augmented Reality and Machine learning - A Secure and Seamless Approach

Digitalization of Objects using Augmented Reality and Machin...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Vashishtha, Srishti Jain, Sarthak Sujal Singh, Sunny Gupta, Vasu Arora, Shaveta Computer Science and Engineering Bharati Vidyapeeth's College of Engineering Delhi India Computer Science and Engineering The NorthCap University Gurgaon India

ISBN: (纸本)9798350375237

With the ascent of digitalization and artificial intelligence, there is an increasing demand for intelligent systems across industries. Machine Learning (ML) drives this evolution by enabling computers to learn autonomously without explicit programming, thereby amplifying the capabilities of digital systems. Moreover, ML enhances cybersecurity by aiding anomaly detection, threat identification, and pattern recognition, bolstering digital security measures. Augmented Reality (AR) engines, powered by ML advancements, transform human-computer interaction. AR's capabilities extend beyond projecting digital imagery into the physical world;it now accurately identifies individuals and objects, removes backgrounds seamlessly, and transmits results securely. Our innovative app revolutionizes AR by seamlessly transposing physical objects into digital environments by using BASNet for Scale Invariant Feature Transformation (SIFT) and detecting objects using YOLO v7. By eliminating the need for third-party services and facilitating direct scanning of physical objects into digital environments, the app ensures optimal pixel quality. Moreover, it prioritizes cybersecurity by employing secure transmission protocols and encryption standards to safeguard data during transfer. This seamless fusion of AR and ML not only enhances user experiences but also fortifies digital security, marking a significant step forward in technological advancement. © 2024 IEEE.

关键词： Machine learning

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