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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Dineshkumar, R. Aluvala, Srinivas Srinath, Suma Alsalami, Zaid Krishnaprasath, V.T. Saveetha Institute Of Medical And Technical Sciences Saveetha University Saveetha School Of Engineering Department Of Ece Chennai India Sr University School Of Computer Science And Engineering Telangana Warangal India Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nehru Institute Of Engineering And Technology Department Of Artificial Intelligence And Data Science Coimbatore India

ISBN: (纸本)9798350381665

Semi-conductor manufacturing companies are growing rapidly by forming collaborative design and wafer fabrication. The yield prediction techniques use production data and provide improved operational efficiency and decrease production costs. Random forest classification method is proposed for semiconductor manufacturing Final Test (FT) yield prediction. data pre-processing methods include Gaussian mixture Model, one hot encoder and Label encoder whereas improved grid search algorithm is used for feature selection. The selected features are then given to the implemented classification Random forest (RF) model. The RF method was evaluated by conducting experiments for three different products which are provided by Silicon Laboratories. The RF method outperformed in handling both numerical and categorical production data and is evaluated by the metrics such as accuracy, precision, recall and F-measure. The developed method achieved 0.985 of accuracy when compared with the existing methods such as LSTM-AM and SH-DNN. © 2024 IEEE.

关键词： Gaussian distribution

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Anomaly Detection in Social networks using Improved Local Outlier Factor 1

Anomaly Detection in Social Networks using Improved Local Ou...

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

作者： Rana Veer Samara Sihman Bharattej, R. Mhsnhasan, Muntader Sowmya, M. Prabhakaran, M.V. Ghamya, K. Saint Leo University Department of Computer Science FL United States The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

In the present era, Anomaly Detection (AD) aims to identify malicious activities in social networks. Even though previous researchers suggest effective methods for AD still there are limitations such as a lack of labeled data and struggled to handle high dimensional data. In this research, Improved Local Outlier Factor (ILOF) is proposed to detect anomalies and effectively handles the high dimensional data by using distance metric to calculate local density. Primarily, the data is collected from CERT insider threat and then preprocessed with truncation to remove extreme values for effective feature extraction. After that, the Principal Component Analysis (PCA) is incorporated to extract most informative features such as principal components to identify anomalies as data points. Finally, the extracted features are fed into proposed ILOF uses density-based approach to calculate the local density effectively for detecting anomalies in social networks. From the results, proposed ILOF model attained better outcomes compared to existing Bidirectional Long Short-Term Memory (BiLSTM) in terms of accuracy, precision, recall and F1-score as 98.50%, 97.08%, 98.25% and 97.66% respectively. © 2025 IEEE.

关键词： Principal component analysis

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Big data Analytics for Quality Control in The Pharmaceutical Industry 10

Big Data Analytics for Quality Control in The Pharmaceutical...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Malathi, P. Jansi, S. Prabhakaran, M.V. Senbagam, K. Sujatha, M.P. Sadaieswaran, R. Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Tamil Nadu Chennai India Acharya college of Engineering and Technology Department of Computer science and Engineering Achariyapuram Puducherry India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Mamallapuram India Agni College of Technology Department of Computer Science and Engineering Chennai Thalambur India Tagore College of arts and science Department of Computer Applications Chennai Chromepet India

ISBN: (纸本)9798350384369

In order to ensure the safety and manufacturing efficiency of pharmaceuticals, rigorous quality control measures are necessary. However, existing quality control systems often struggle with industrial process complexity and regulatory requirements. The proposed study highlights the need for a revolutionary strategy to overcome the limitations of existing systems. Quality control systems in existing systems have limitations such as real-time monitoring, insufficient data analysis for pattern identification, and limited predictive capabilities. These limitations have an impact on the industry's ability to recognize and manage quality concerns, posing a danger to medicine manufacturing. For addressing these issues, a novel approach to maintain quality control in the pharmaceutical sector using big data analytics is proposed. Advanced analytics are used for real-time monitoring, data analysis and predictive modeling in the proposed system. The results and analysis through extensive comparisons shows proposed big data analytics system achieves 95% fault detection, 40% downtime reduction, and 35% waste reduction in pharmaceutical manufacturing. The proposed system's ability to handle massive datasets allows it to identify complicated patterns and abnormalities, enabling proactive quality control. The implementation exceeds industry standards, addressing data privacy and security concerns. © 2024 IEEE.

关键词： data quality

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A Fuzzy C-Means With Support Vector Regression for Calibration and Fault Detection in Modern Instruments 4

A Fuzzy C-Means With Support Vector Regression for Calibrati...

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4th IEEE International Conference on Mobile networks and Wireless Communications, ICMNWC 2024

作者： Habelalmateen, Mohammed I. Kasarla, Satish Reddy Sravanthi, Jajimoggala Kavitha, P. Sundaram, S. Meenakshi College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Hyderabad Institute of Technology And Management Department of Electronics and Communication Engineering Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India

ISBN: (纸本)9798350352931

From the past few decades, Calibration and Fault Detection in modern instruments are crucial for ensuring accuracy, reliability, and safety by configuring instruments to provide accurate measurements and identifying anomalies or malfunctions. These processes prevent accidents, reduce downtime, and increase productivity, but also have drawbacks, including being time-consuming and labor-intensive, requiring specialized expertise and equipment, and being expensive, while also being susceptible to environmental factors and requiring frequent recalibration. To overcome these problems, a Fuzzy C-Means with Support Vector Regression (FCM-SVR) is proposed for detecting calibration and fault in modern instruments using Predictive Electrical Fault Analysis (PEFA) dataset. The collected data is undergone preprocessing using Label Encoder (LE) to normalize data without reducing the dimension. Then, Empirical Mode Decomposition and Sample Entropy are considered for feature extraction for robust fault detection and to reduce false alarm. At last, FCM-SVR is employed as classifier for enhancing computational efficiency, flexibility, and overall performance. From the results, it clearly shows that the proposed FCM-SVR obtained higher values in the context of precision, recall, F1 score and accuracy when compared to existing method K-Nearest Neighbor (KNN). © 2024 IEEE.

关键词： Calibration

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Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory 1

Toxic Comment Detection based on Improved Bi Directional Lon...

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

作者： Kamma, Vidya Wbaid, Saef Sudhakar, K. Nisha, A. Saravanan, T. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural networks (CNN)-BiLSTMs. © 2025 IEEE.

关键词： Semantics

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Cardio-Vascular Disease Prediction using Machine Learning Techniques

Cardio-Vascular Disease Prediction using Machine Learning Te...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Konda, Srinivas Kar, Naresh Kumar Pulicherla, Padmaja Shivakanth, Gandla Ramachandra, C.A. Cmr Technical Campus Department of Data Science Hyderabad India Gitam Deemed to Be University Department of Cse Hyderabad India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

The main goal of this study is to use data Mining Method and Artificial Neural Network to develop a system that can automatically and rapidly predict the risk of coronary heart disease (ANN). The IRT Perundurai Medical College and Hospital's master health checkup data on occupational drivers were used to test this idea (PMCH). Analysis for risk identification is performed in the first stage of the hybrid approach suggested in this study, and level prediction is performed in the second. The sensitivity, specificity, precision, receiver operating curve, area under curve, 10-fold cross validation technique, and the F-measure are used for this investigation. The initial step of the study involves thinking about the most common and changeable dangers. Systolic blood pressure, diastolic blood pressure, and body mass index (BMI) are three biophysical variables, whereas fasting blood sugar, postprandial blood sugar, and triglyceride levels are three blood chemical factors (TG). All of these characteristics have a predetermined margin value that is based on WHO guidelines. Support Vector Machine (SVM), Naive Bayes (NB), and the C4.5 algorithm in Decision Tree are the three approaches used to categorize these variables and forecast the risk (DT). The C4.5 algorithm fared best in forecasting CHD risk when the three approaches were compared using the performance metrics, as discovered by the investigation. The decision tree C4.5 method outperformed the other two classifiers with an improved 99.5% accuracy and 99.67% sensitivity. The increased percentage demonstrates that the Decision tree method delivered consistent results that were better to those produced by the Naive Bayes and SVM models. © 2023 IEEE.

关键词： Decision trees

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Understanding ReLU Network Robustness Through Test Set Certification Performance

Understanding ReLU Network Robustness Through Test Set Certi...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Nicola Franco Jeanette Miriam Lorenz Karsten Roscher Stephan Günnemann Fraunhofer Institute for Cognitive Systems IKS Munich Germany Dept. of Computer Science & Munich Data Science Institute Technical Univ. of Munich Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks can be vulnerable to small changes in input within their learning distribution, and this vulnerability increases for distributional shifts or input completely outside their training distribution. To ensure networks are used safely, robustness certificates offer formal assurances about the stability of their predictions in a pre-defined range around the input. However, the relationship between correctness and certified robustness remains unclear. In this work, we investigate the unexpected outcomes of verification methods applied to piecewise linear classifiers for clean, perturbed, in- and out-of-distribution samples. In our experiments focused on image classification, we observed that introducing a modest stability margin around the input sample leads to an important reduction in misclassified samples — approximately a 75% decrease — compared to the roughly 11% for samples that are correctly classified. This finding emphasizes the value of formal verification methods as an extra layer of safety, illustrating their effectiveness in enhancing accuracy for data that falls within the distribution. On the other hand, we provide a theoretical demonstration that formal verification methods robustly certify samples sufficiently far from the training distribution. These results are integrated with an experimental analysis and demonstrate their limitations compared to standard out-of-distribution detection methods.

关键词： Accuracy Perturbation methods Neural networks Reliability theory Robustness Stability analysis Safety

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Preserving Privacy using Distributed Ledger Technology in Intelligent Transportation System 2022

Preserving Privacy using Distributed Ledger Technology in In...

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14th International Conference on Contemporary Computing, IC3 2022

作者： Nair, Meghna Manoj Frederick, Terrance Fernandez Tyagi, Amit Kumar School of Computer Science Engineering Vellore Institute of Technology Tamilnadu Chennai India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Centre for Advanced Data Science Vellore Institute of Technology Tamilnadu Chennai600127 India

ISBN: (纸本)9781450396752

Intelligent Transportation System (ITS) is an enhanced application which mainly aims towards the curation of smart and interconnectivity services to ease the existing transportation systems. It is a field with a plethora of future possibilities so as to provide efficient and advanced techniques by solving and tackling the existing challenges and problems. Especially in cities and countries which aim towards developing smart cities, ITS plays an essential role in advancing the transportation sector with regards to the city. Using a number of AI and ML techniques, a variety of approaches, algorithms and techniques can be put to effective utilization to boost the ITS ecosystem. However, with every boon compliment few banes and plausible bottlenecks. One of the major concerns while using V2V networks is the privacy and security issue. The data being transferred amidst vehicles mainly caters to the status updates on the vehicles adjacent to it, the speed, and driver's information in case of any accidents or wherever required. In such cases, the privacy of the drivers details and personal information are at stake and often poses a major security challenge. Furthermore, there are chances of data breaches and similar attacks by malicious users which can lead to huge data loss and tampering of data. A possible solution to this problem is to approach it through the perspective of Blockchain. Blockchain is a concept of sharing information/ data in a distributed and decentralized manner. The numerous blocks present in a blockchain which are responsible for storing information do so by encrypting and securing the data and hence can be integrated with a V2V system for overcoming the privacy and security challenges. © 2022 ACM.

关键词： Blockchain

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Metaheuristic Based Clustering with Deep Learning Model for Big data Classification

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computer Systems science & Engineering 2023年第1期44卷 391-406页

作者： R.Krishnaswamy Kamalraj Subramaniam V.Nandini K.Vijayalakshmi Seifedine Kadry Yunyoung Nam Department of Electronics and Communication Engineering University College of Engineering AriyalurAriyalur621704India Department of Biomedical Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatore641021India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600077India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two *** the same time,clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in ***-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial *** data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce *** this motivation,this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics(MR-HDBCC).The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big *** addition,the MR-HDBCC technique involves three distinct processes namely pre-processing,clustering,and *** proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise(DBSCAN)techni-que which is capable of detecting random shapes and diverse clusters with noisy *** improving the performance of the DBSCAN technique,a hybrid model using cockroach swarm optimization(CSO)algorithm is developed for the exploration of the search space and determine the optimal parameters for density based ***,bidirectional gated recurrent neural network(BGRNN)is employed for the classification of big *** experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.

关键词： Big data data classification clustering mapreduce dbscan algorithm

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Random Forests for Predicting Diabetes Progression and Complications 3

Random Forests for Predicting Diabetes Progression and Compl...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Ranganathan, Chitra Sabapathy Nandhini, R. Nisha, K. Sumathi, G. Sudhakar, G. Srinivasan, C. Mphasis Corporation Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

The main objective of the presented work in this paper is to use Random Forests as an alternative biomarker for predicting the development of diabetes. Identifying high-risk patients is crucial for improving patient outcomes and reducing healthcare expenses. The proposed system improves diabetes treatment via the use of predictive analytics, which promotes preventative and early intervention-focused healthcare practices. It helps manage the risks associated with diabetes by using machine learning, which is part of a larger movement towards precision medicine. This method shows promise to enhance long-term health outcomes, optimize resource allocation, and provide personalized treatment to patients. Findings from the Pima Indians Diabetes database quantify the risks of diabetes and patient characteristics. Blood pressure, glucose, insulin, and body mass index are all taken from each patient. The dangers of retinopathy, neuropathy, and nephropathy are also numerically encoded, with 0 indicating minimal risk, 1 medium risk, and 2 high risks. HbA1c is also included. The blood pressure ranges from 115/78 to 130/85mmHgA1c levels, the body mass index (BMI) from 26 to 32 mm, and the glucose level from 110 to 140 mm Hg. © 2024 IEEE.

关键词： Blood pressure

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