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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Sreekanth, G.R. Latha, R.S. Roja, R. Sankarnanth, S. Gayathri, A. Kongu Engineering College Department of Computer Science and Engineering Erode India Kongu Engineering College Department of Computer Technology Erode India

ISBN: (纸本)9798350349221

Corn, a grain categorized within the grass family, stands as a fundamental staple crop globally. It plays a crucial role in supplying sustenance for both humans and livestock, in addition to serving as a raw material for various industries. In 2017, Africa produced 7.4% of the 1135 million tons produced worldwide in 40 million hectares. Throughout its growing process, corn plant is impacted by numerous illnesses. Lack of early detection of its diseases may result in a loss in productivity and even crop failure. It is proposed to analyze the leaves of the corn plant for identifying the diseases by exploiting deep learning methods. A wide range of deep learning algorithms are being utilized to detect the corn leaf with infection with good range of accuracy. To categorize the leaf, image processing models are mostly used for these kind of disease detections. Since it has in-built convolutional layer, CNN can reduce the image with high dimensionality without losing any kind of information. In this proposed work CNN with Adam optimizer has been implemented for better results and achieved 91.68 % of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Designing Secure IoT Systems: A Survey of Existing Approaches and Future Directions 2

Designing Secure IoT Systems: A Survey of Existing Approache...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Praveen, T.N.V.S. Odugu, Rama Devi Lingamgunta, Sumalatha Jntuk Department of Computer Science &engineering Kakinada India Lakireddy Balireddy College of Engineering Department of Computer Science &engineering Mylavaram India

ISBN: (纸本)9798350328202

The Internet of Things (IoT) has emerged as a transformative technology, connecting a wide array of devices and enabling seamless communication and data exchange. However, the rapid proliferation of IoT devices has brought forth significant security challenges, making them susceptible to various cyber-attacks. This research paper explores the design of security schemes for the detection and prevention of attacks on IoT systems. It examines the major threats faced by IoT deployments and proposes comprehensive security measures to safeguard these interconnected devices and networks. The proposed schemes encompass a multi-layered approach, including intrusion detection systems, secure communication protocols, and encryption techniques, to mitigate the risks and enhance the overall security posture of IoT ecosystems. © 2024 IEEE.

关键词： Data visualization

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Optimized graph transformer with molecule attention network based multi class attack detection framework for enhancing privacy and security in WSN

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Multimedia Tools and Applications 2025年第15期84卷 14273-14304页

作者： Anoop, M. Mary, L. William Wilson, Allan J. Kiran, W.S. Department of Computer Science and Application SRM Institute of Science and Technology Ramapuram campus Chennai600089 India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India

In today's rapidly evolving network landscape, cybersecurity has become increasingly crucial. However, wireless sensor networks face unique challenges due to their limited resources and diverse composition, high computational demands, diminished accuracy rates, making them susceptible to a wide array of cyber threats. To tackle these challenges, this research introduced a novel method known as Optimized Graph Transformer with Molecule Attention Network (OGTMAN) on the fusion of Secure Multi-party Computation (SMC) and differential privacy aimed at enhancing privacy and security. This approach starts with data preprocessing, employing min–max normalization to address dataset imbalances. N-Tuple Contrastive Learning with Generative transformer(N-TCLG)feature extractor distinguish between different classes of data instances and effectively learns representations that capture relevant information about network traffic patterns Further, feature selection by enhanced chi-square statistics with kepler algorithm, is utilized to assess the significance of each feature in characteristic between normal network behavior and malicious activities further refines the feature selection method by optimizing the selection criteria based on the statistical properties of the dataset. Finally, the proposed OGTMAN classifies various attacks based on their behavioural patterns with the help of enhanced ant aphid optimization with adaptive step factor. Ultimately, this fine-tuning yield superior model training outcomes, resulting more accurate attack classification outcomes. To evaluate the effectiveness of this approach, datasets encompassing with KDD Cup 99 datasets are employed. The efficiency of the introduced method is demonstrated when compared with prevailing attack detection methods in terms of accuracy, recall, precision and F-measure of 99.98%, 99.5%.,99.9% and 99.5% respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer N

关键词： Molecules

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Classification of System Failures in Cyber-Physical Systems Using Heterogeneous Edge Enhanced Graph Attention Networks 3

Classification of System Failures in Cyber-Physical Systems ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Obidhusin, Saef Chandra, N. Subhash Balamurugan, D. Premalatha, J. Kumararaja, K. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Computer Science and Engineering Salem India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

From the past few years, Cyber-Physical Systems (CPS) have rapidly evolved in various sectors by integrating computing, networking, and physical processes. CPS facilitates the management of critical infrastructure such as transportation, healthcare, and energy. Even though there are advanced technologies, which are vulnerable to failures led to severe consequences including unpredictable interactions between cyber and physical components. Therefore, to address these issues a robust model is proposed by using Heterogeneous Edge-Enhanced Graph Attention Network (HEE-GAT). Initially, the data is collected from BoT-IoT database which consists of huge traffic data. Then, the collected data is preprocessed using Z-score normalization is applied to standardize feature distributions while preserving network traffic characteristics. Furthermore, for selection of significant features Skill Optimization Algorithm (SOA) is utilized. Finally, HEE-GAT is utilized for effective classification of system failures in CPS. Unlike existing failure detection models such as LSTM-GNN hybrids, which struggle with capturing heterogeneous dependencies and dynamic edge relationships in CPS data, HEE-GAT enhances graph-based learning by assigning dynamic edge weights and utilizing attention mechanisms to distinguish between different failure types with higher accuracy. The proposed HEE-GAT model acquired accuracy of 95% when compared to the existing Fractional Artificial Protozoa Optimization enabled Spiking VGG-16 (FAPO Spiking VGG-16). © 2025 IEEE.

关键词： Service oriented architecture (SOA)

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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue Robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Hybrid Approach for Cost Efficient Application Placement in Fog-Cloud Computing Environments

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computers, Materials & Continua 2024年第6期79卷 4127-4148页

作者： Abdulelah Alwabel Chinmaya Kumar Swain Department of Computer Sciences College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Institute of Management and Information TechnologyCuttackBPUTOdishaIndia

Fog computing has recently developed as a new paradigm with the aim of addressing time-sensitive applications better than with cloud computing by placing and processing tasks in close proximity to the data ***,the majority of the fog nodes in this environment are geographically scattered with resources that are limited in terms of capabilities compared to cloud nodes,thus making the application placement problem more complex than that in cloud *** approach for cost-efficient application placement in fog-cloud computing environments that combines the benefits of both fog and cloud computing to optimize the placement of applications and services while minimizing *** approach is particularly relevant in scenarios where latency,resource constraints,and cost considerations are crucial factors for the deployment of *** this study,we propose a hybrid approach that combines a genetic algorithm(GA)with the Flamingo Search Algorithm(FSA)to place application modules while minimizing *** consider four cost-types for application deployment:Computation,communication,energy consumption,and *** proposed hybrid approach is called GA-FSA and is designed to place the application modules considering the deadline of the application and deploy them appropriately to fog or cloud nodes to curtail the overall cost of the *** extensive simulation is conducted to assess the performance of the proposed approach compared to other state-of-the-art *** results demonstrate that GA-FSA approach is superior to the other approaches with respect to task guarantee ratio(TGR)and total cost.

关键词： Placement mechanism application module placement fog computing cloud computing genetic algorithm flamingo search algorithm

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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 HR: Ensemble & Deep Learning Methods for Strategic Employee Retention 3

Predictive HR: Ensemble & Deep Learning Methods for Strategi...

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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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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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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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