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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Kumar, Krithiga Ganesh Harini, S. Perumalraja, R. Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798350380583

In the rapidly evolving pharmaceutical industry, optimizing supply chain management is critical for enhancing efficiency and customer satisfaction. This research focuses on a data-driven approach to improving supply chain analytics by emphasizing two key areas: demand forecasting and customer segmentation. The project aims to uncover hidden patterns for developing predictive models through statistical techniques and machine learning algorithms, leveraging historical sales and customer data. By providing more accurate demand forecasts and classifying customers based on purchasing behavior, these models facilitate targeted and effective marketing strategies. Further, we formulate a multi-echelon inventory management algorithm for minimizing the inventory kept at all stages within the supply chain, while reducing costs without compromising service levels. The methodologies thus complement each other for building a resilient and flexible response to demand fluctuations in the market. Last, but not least, this research gives useful insights to pharmaceutical companies looking to improve their operational efficiency while meeting customer needs in a competitive environment. © 2024 IEEE.

关键词： Commerce

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The Agro Predictor - A Smart farming approach using Deep Learning

The Agro Predictor - A Smart farming approach using Deep Lea...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Sheela, K. Santha Threegha, S. Yazhmozhe, G.M. Velammal College of Engineering and Technology Computer Science Engineering Madurai India

ISBN: (纸本)9798350309140

The prime focus of this Agro-Predictor system which relies on the key learnings of the Internet of Things and Deep Learning Approach, to benefit the agricultural sector, placing its undivided attention on critical areas. Analysis of soil types and different weather conditions affecting, recommendations based on all the predictions made for the best production and farming. The integration of advanced technologies like Deep Learning, Data Mining, IOT and etc, has influenced agricultural sector transformation in various ways. The potential of deep learning models has been demonstrated to revolutionize farming practices by collecting the required data set, training and segregating the following, and predicting the required outcomes with the best recommendations. The work highlights the need for innovative solutions to address the growing global demand by identifying problem areas that require further research for sustainable agriculture by predicting various factors to reach the required solutions in farming. © 2024 IEEE.

关键词： Smart agriculture

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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High Utility Periodic Frequent Pattern Mining in Multiple Sequences

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computer Modeling in engineering & sciences 2023年第10期137卷 733-759页

作者： Chien-Ming Chen Zhenzhou Zhang Jimmy Ming-Tai Wu Kuruva Lakshmanna College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Technology Vellore Institute of TechnologyVellore632014India

Periodic patternmining has become a popular research subject in recent years;this approach involves the discoveryof frequently recurring patterns in a transaction sequence. However, previous algorithms for periodic patternmining have ignored the utility (profit, value) of patterns. Additionally, these algorithms only identify periodicpatterns in a single sequence. However, identifying patterns of high utility that are common to a set of sequencesis more valuable. In several fields, identifying high-utility periodic frequent patterns in multiple sequences isimportant. In this study, an efficient algorithm called MHUPFPS was proposed to identify such patterns. To addressexisting problems, three new measures are defined: the utility, high support, and high-utility period sequenceratios. Further, a new upper bound, upSeqRa, and two new pruning properties were proposed. MHUPFPS usesa newly defined HUPFPS-list structure to significantly accelerate the reduction of the search space and improvethe overall performance of the algorithm. Furthermore, the proposed algorithmis evaluated using several *** experimental results indicate that the algorithm is accurate and effective in filtering several non-high-utilityperiodic frequent patterns.

关键词： Decision making frequent periodic pattern multi-sequence database sequential rules utility mining

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Integration of Cross-Chain technology-Based High-Performance Blockchain Network for Industrial Internet of Things 4

Integration of Cross-Chain Technology-Based High-Performance...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Kothari, Rakshit Jain, Kalpana Choudhary, Naveen College of Technology and Engineering MPUAT Computer Science and Engineering Udaipur India

ISBN: (纸本)9798350353266

The Internet of Things (IoT) has emerged as a promising technology for connecting physical objects and enabling communication between them. However, IoT faces several challenges such as security, privacy, and interoperability that need to be addressed before large-scale deployment. Blockchain has the potential to address these challenges due to its decentralized architecture, cryptographic security, immutability and consensus mechanisms. This paper explores the integration of blockchain technology with IoT for industrial applications. A cross-chain architecture is proposed to build a high-performance blockchain network customized for the needs of Industrial IoT (IIoT). The limitations of existing blockchain platforms like low transaction throughput are overcome by improvements in the consensus algorithm and employing a multi-chain structure. A proof-of-stake based consensus is implemented to achieve fast consensus and improve scalability. Cross-chain communication protocols are designed to enable interoperability between heterogeneous blockchains. The experimental evaluation demonstrates that the resulting blockchain network can achieve over 1000 transactions per second throughput per chain while maintaining decentralization, security and low latency. The cross-chain structure enables this performance to scale as the network expands. The paper provides comprehensive technical details of the various components of the blockchain platform including consensus, cross-chain communication and network structure. The designed blockchain architecture can potentially enable several IIoT use cases by enhancing security, accessibility, automation and coordination of industrial systems. © 2024 IEEE.

关键词： Blockchain Network Cross-Chain technology Industrial Internet of Things Practical Byzantine Fault Tolerance Proof-of-Work (PoW) Proof-ofStake

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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

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computer Systems science & engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Garden Irrigation System Using IOT 11

Garden Irrigation System Using IOT

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Murali, S. Anchitaalagammai, J.V. Kavitha, S. Astro Flavin, P. Sanjay Karthick, S. Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350350357

Addressing the global challenges of water resource depletion and efficient agricultural water management has led to the emergence of an innovative solution - the integration of Internet of Things (IoT) technology into irrigation systems. This abstract introduces a smart irrigation system designed to harness IoT capabilities, ultimately optimizing water utilization and enhancing crop yields. The proposed smart irrigation system comprises several key components: sensor nodes, actuators, a central controller, and cloud-based data analytics. Soil moisture sensors are placed in field, continuously monitoring soil moisture levels in real-time. These sensors transmit the collected data wirelessly to a central controller. This central controller, equipped with predefined thresholds and crop-specific parameters, processes the incoming data to determine irrigation requirements. To control water flow within the irrigation system, the system employs actuators like water pumps or valves. The central controller makes data-driven decisions, activating or deactivating these actuators based on sensor data analysis. Moreover, the system has the capability to incorporate weather forecast data to dynamically adjust irrigation schedules. Crucially, cloud-based data analytics play a pivotal role in this system. They store and analyze the collected data using advanced algorithms and machine learning techniques. This analysis identifies patterns, optimizes irrigation schedules, and generates actionable insights for farmers. The system also enables notifications and alerts, which can be sent to farmers through mobile applications or web interfaces, allowing them to remotely monitor and manage the irrigation process. © 2024 IEEE.

关键词： Controllers

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Early Breast Cancer Prediction Using Machine-Learning Algorithms

Early Breast Cancer Prediction Using Machine-Learning Algori...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Abirami, S. Sri Vignesh, R.B. Srihariprasath, V. Mathialahan, U. Yaswanth, T.S. Computer Science And Engineering Sri Krishna College Of Engineering And Technology India

ISBN: (纸本)9798350364828

Breast cancer poses a significant global health challenge, affecting millions of women each year and leading to substantial cancer-related deaths. Early detection is crucial, particularly in regions with limited resources and inefficient healthcare systems where late-stage diagnoses are common. To tackle this issue, we propose a novel approach that employs machine-learning algorithms for early breast cancer prediction. Our model integrates a diverse ensemble of machine-learning algorithms, including K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), and Decision Tree Classifier. This ensemble harnesses the collective predictive power of these algorithms to distinguish between benign and malignant tumor types, enabling early diagnosis and prompt intervention. Furthermore, our approach enhances predictive capabilities by incorporating Logistic Regression, Random Forest, and additional KNN algorithms. Each algorithm contributes unique strengths to the ensemble: SVM excels in identifying complex data relationships, Logistic Regression offers interpretability, Random Forest handles large datasets and captures intricate patterns, while KNN adapts well to irregular decision boundaries. By leveraging the strengths of these diverse algorithms, our proposed system provides a more comprehensive and accurate breast cancer prediction model. Through ensemble learning, we mitigate individual algorithm weaknesses and improve predictive performance, thereby advancing the effectiveness of early breast cancer detection and ultimately improving patient outcomes. Keywords: Machine Learning, Breast Cancer Prediction, Ensemble Learning, K-Nearest Neighbor (KNN), Naive Bayes, Support Vector Machines (SVM), Decision Tree Classifier, Logistic Regression, Random Forest, Early Detection, Healthcare Systems © 2024 IEEE.

关键词： Learning systems

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Web service discovery with incorporation of web services clustering

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International Journal of computers and Applications 2023年第1期45卷 51-62页

作者： Jalal, Sunita Yadav, Dharmendra Kumar Negi, Chetan Singh Department of Computer Engineering College of Technology GBPUAT Pantnagar India Department of Computer Science and Engineering MNNIT Allahabad Prayagraj India

With the rapid growth and adoption of web-based technologies, more and more service providers offer various services on the web. This can facilitate the organizations to develop business applications using web services composition over the Internet according to their business requirements. The primary requirement for developing composite web service is to discover the most relevant web service for realizing each function of the required composite web service. Finding the most relevant web service is still a challenging task. In this paper, we propose web service discovery approach that incorporates web services clustering. Clustering technique combines latent Dirichlet allocation(LDA) and k-Medoids and reduces search space for web service discovery. Web service discovery uses a match-making algorithm that utilizes WordNet database and Hungarian method to compute the semantic similarity score for a web service based on the requested query. Experimental results show that the proposed approach performs better than LDA-based approach. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Web services

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Advance Genome Disorder Prediction Model Using Deep Learning Algorithms 3

Advance Genome Disorder Prediction Model Using Deep Learning...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Ponmalar, S. Priyadharshini, K. Pooja, V. Swathi, M. Velammal College of Engineering and Technology Computer Science and Engineering Madurai India

ISBN: (纸本)9798350381931

A comparative analysis of deep learning techniques for the prediction of genetic disease - cystic fibrosis. Cystic fibrosis (CF) is a complex genetic disorder that needs early and accurate diagnosis for effective treatment. To achieve this, we introduce a powerful genome disorder prediction model that uses advanced deep learning techniques. Our model is built on two key algorithms: Residual Neural Network (ResNet) and Extreme Learning Machine (ELM). We collected a comprehensive dataset that includes genetic data, clinical records, and information about the environment. Our goal was to improve the accuracy of CF prediction. After extensive testing and cross-validation, our research shows that our model performs better than traditional methods. This study is a significant step toward improving the diagnosis and personalized management of cystic fibrosis © 2024 IEEE.

关键词： Forecasting

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