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

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

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

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

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

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.

关键词： Accuracy Sensitivity Semantics User-generated content Bidirectional control Feature extraction Vectors Convolutional neural networks Intelligent systems Long short term memory

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A Post-processing Ensemble Machine Learning Approach for Prediction and Classification of Cardiovascular Disease

A Post-processing Ensemble Machine Learning Approach for Pre...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Pradip Chakraborty Bikash Kanti Sarkar Myasar Mundher adnan S Srikanth S meenakshi Sundaram Department of Computer Science and Engineering Birla Institute of Technology Mesra India The Islamic university Najaf Iraq Department of Electronics and Communication Engineering Mallareddy Engineering College for Women (Autonomous) Hyderabad Telangana India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Cardiovascular Disease (CVD) is one of the dangerous diseases which affecting the human health. CVD is the significant cause of mortality as well as morbidity in the population of the world. The prediction and diagnosis of CVD is challenging in clinical data analysis as well as health care providers to avert the patient from diminishing like disease as well as preserve lives. An automatic detection for the classification of arrhythmia performs a significant role in managing as well as curing the cardiovascular diseases. Therefore, this research proposes the post-processing ensemble Machine Learning (ML) approach for the prediction and classification of CVD. Initially, this research collected the publicly available Kaggle dataset of CVD for validate the effectiveness of the model. Then, handling missing values as well as data unbalancing techniques are used for the data pre-processing. After that, Principal Component Analysis (PCA) is used for the selection of relevant features. Finally, post-processing ensemble ML approach is used for the classification of CVD. The proposed method is estimated by various performance metrices and it achieves the accuracy of 99.56% when compared to the previous approaches such as Support Vector Machine (SVM) and Gradient Boosting (GB).

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Privacy Preserving Analytics in IoT Systems using Federated Learning with Homomorphic Encryption and Differential Privacy

Privacy Preserving Analytics in IoT Systems using Federated ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Abuthar Mahmood Sasikumar Gurumoorthy Meghana A Yogesh Ramaswamy Gayathiri B Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Tiruchirappalli India Department of Electrical and Electronics Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In recent years, privacy preserving analytics has improved a lot which leads to development of smart platforms that include advanced mobile devices which are used by many people for businesses that are highly secured. These devices run apps that helps to improve online services which making things faster but some devices failed in securing data and maintaining privacy. To overcome those challenges, this research proposes a model namely Federated Learning with Homomorphic Encryption and Differential Privacy (FL-HE-DP). Initially, the data is collected from Telemetry and Network-Internet of Things (ToN-IoT) which contains nine different kinds of attacks, legitimate and offensive events in network systems. Then, the collected data is preprocessed using K-Nearest Neighbor (KNN) imputation, min-max scaling and one hot encoding which assists in handling noise, normalization and for converting categorical values into numerical values. Further, the preprocessed data is embedded into federated learning which utilizes HE for edge aggregations and encryption. Finally, the encrypted data is fed to DP, which helps to prevent data and adversarial attacks. The proposed FL-HE-DL acquired greater results by attaining Root Mean Squared Error (RMSE) of 8.965, Mean Absolute Error (MAE) of 7.654 and Mean Squared Error (MSE) of 5.764 respectively when compared to existing Soft Actor Critic (SAC) model.

关键词： Differential privacy Privacy Federated learning Computational modeling Nearest neighbor methods Imputation Encoding Numerical models Homomorphic encryption Integrated circuit modeling

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Traffic Sign Recognition using You Look Only Once Version 8 with Vision Transformer

Traffic Sign Recognition using You Look Only Once Version 8 ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Mohammed Ihsan Ramesh Babu N Navamani C Narendra Chennupati P. Vinayasree Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Nandha Engineering College Erode India Hindsight Software Solutions Inc Frisco TX Department of Computer Science and Engineering School of Engineering Anurag University Hyderabad India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

In recent years, Traffic Sign Recognition (TSR) has become an emerging computer vision task which allows vehicles to detect and interpret road signs from captured images that facilitates for safe navigation. Even though, previous researchers have developed effective models to classify and recognize traffic signs under varying conditions, but still there are challenges such as occlusions, motion blur and luminous conditions. To overcome these challenges, this research proposes a hybrid approach using the proposed You Look Only Once Version 8 with Vision Transformer (YOLOV8-ViT) model. Initially, the data is collected from Tsinghua-Tencent 100K (TT100K) dataset which comprises of vast traffic signs in varying luminous and weather conditions. Then, the collected data is preprocessed with data augmentation and Contrast Limited Adaptive Histogram Equalization (CLAHE) for improving generalizability and visibility in poor lighting conditions respectively. Further, Convolution with Attention Network (CoAtNet) is utilized for extraction of feature which has the ability to capture spatial and long-range dependencies. Finally, the proposed YOLOV8-ViT is incorporated for TSR as it handles complex backgrounds and occlusions effectively. The proposed YOLOV8-ViT acquired better results with 98.9% accuracy, 97.9% precision, 96.6% recall, 97.5% F1-score respectively when compared with the existing TrafficSignNet model.

关键词： Adaptation models computer vision Histograms Image recognition Computational modeling Transformers Feature extraction Data augmentation Data models Integrated circuit modeling

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Predictive Analytics for Crime Prevention in Smart Cities Using Machine Learning

Predictive Analytics for Crime Prevention in Smart Cities Us...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Ponugoti Kalpana Sarangam Kodati Nara Sreekanth Hassan Mohamed Ali Ramachandra A C Department of Computer Science and Engineering AVN Institute of Engineering and Technology Hyderabad India Department of Information Technology CVR College of Engineering Hyderabad India Department of Computer Science and Engineering BVRIT Hyderabad College of Engineering for Women Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Crime prevention in smart city infrastructure significantly impacts improving quality of human life. However, the substantial increase in the urban population in recent years affects accuracy, safety, and security. This paper presents a solution using the Hyperplane to improve the Radial Basis Function with Support Vector Machine (SVM) based on Machine Learning techniques to enhance accuracy, safety, and security. The SVM method aims to reduce crime rates through proactive measures based on predictive insights. The RBF kernel handles nonlinear relationships by mapping input features into a higher-dimensional space, allowing the model to capture complex patterns. Initially, data is obtained from the Chicago crime dataset and pre-processed by checking for missing values and performing feature scaling to ensure consistency. Feature selection is conducted using Principal Component Analysis (PCA) to identify relevant features from the data. Crime prediction using RBF with SVM is performed efficiently to enhance accuracy. The proposed RBF-SVM model is evaluated using the Chicago crime dataset, achieving a high accuracy of 0.89, a precision of 0.80, a recall of 0.82, and an F1-score of 0.85. The performance is compared to existing techniques such as Decision Tree (DT) and Long Short-Term Memory (LSTM).

关键词： Support vector machines Accuracy Smart cities Prevention and mitigation Feature extraction Safety Security Kernel Long short term memory Principal component analysis

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Continuous Integration, Testing Deployment and Delivery in Devops

Continuous Integration, Testing Deployment and Delivery in D...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： E Naresh S V N Murthy N. Sreenivasa Seshaiah Merikapudi C R Rakhi Krishna Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of CSE SJC Institute of Technology Chikkaballapur Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Yelahanka Bangalore Department of CSE BGSIT

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The proposed research article focuses on Continuous Integration, Testing, Delivery and Deployment in terms of DevOps environment. DevOps is a combination of development and operations. In a usual work environment, both development and operations of that company work entirely differently where mutual interaction is said to be minimum. Thus, to overcome that, DevOps was introduced, where both development and operations must work together in the same environment. In this article we will discuss what each of these terms means, how they work, why they are important in today’s software development life cycle (SDLC), what tools are used for each of them, etc. We will consider a few scenarios where the above-mentioned topics are better than the traditional or the most widely used SDLC methodologies.

关键词： Knowledge engineering DevOps computer bugs Companies Production Continuous integration Software

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Diabetic Retinopathy Detection and Stage Classification Using Lenet-5Architecture

Diabetic Retinopathy Detection and Stage Classification Usin...

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Recent Advances in science and engineering technology (ICRASET), International Conference on

作者： M J Prasannakumar K Priyanka K Smitha M S Vijay Kumar Computer Science and Engineering BGSIT Adichunchanagiri University BG Nagara Mandya INDIA Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore INDIA Artificial Intelligence and Machine Learning BGSIT Adichunchanagiri University BG Nagara Mandya INDIA Information Science and Engineering MIT Mysore INDIA

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

Diabetic retinopathy diagnosis and treatment are made possible through retinal screening. To process the retinal screening, we are developing a deep learning system to detect and classify diabetic *** it is not diagnosed treated it may lead to other serious eye infection like macular edema and glaucoma ultimately causing loss of eye sight. In contrasting to manually created features, we employed the convolutional neural networks (CNN) model i.e Lenet-5 architecture to automatically extract features. Methodologies like edge detection, thresholding and data augmentation are applied to the system during error checking. The efficiency of the system is assessed using statistical metrics like sensitivity (SE), specificity (SP), F-measure, and classification accuracy (ACC).The model achieved an average accuracy of 97%,recall of 22% and f1-score of 65%.

关键词： Deep learning Diabetic retinopathy Accuracy Retina Feature extraction Convolutional neural networks Biomedical monitoring Monitoring Medical diagnostic imaging Asthma

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Cloud Based Automated Control System Workshops and Rooms for Controlling Parameters

Cloud Based Automated Control System Workshops and Rooms for...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Dinesh Kumar Ravinder Singh Kuntal Pragya Deep Anup Shekhar Chamoli Pinki Singh Rishabh Mandal Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad UP Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of AI & DS IIMT College of Engineering Greater Noida India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Department of Computer Science and Engineering Accurate College of Engineering & Management Greator Noida India Department of Computer Science and Engineering Tula's Institute Dehradun UK India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Across all industries, cloud-based automated control systems have completely changed how operational and environmental parameters are tracked and controlled. With cloud-integrated systems for real-time data collection, processing, and control, this concept presents intelligent, remote-controlled workshops and control rooms. To guarantee accurate control of crucial variables like temperature, humidity, pressure, and equipment efficiency, these systems make use of IoT devices, AI techniques, and big data analytics. Through sophisticated data perspectives, the cloud-based strategy enables predictive maintenance, improves flexibility, and lowers operating expenses. Stakeholders may make prompt, data-driven choices from anywhere with remote access to control rooms. These seminars provide practical experience with cloud-controlled settings and function as training and development centres. The system's flexibility to meet a range of industrial demands is further enhanced by its compatibility with external tools and equipment. The design, deployment, and possible uses of cloud-based control systems are covered in this paper, with an emphasis on how they might promote operational excellence, energy efficiency, and smart manufacturing.

关键词： Knowledge engineering Machine control Clouds Control systems Software Machine tools Security Time factors Personnel Standards

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An Effective Stroke Prediction Based on Cat Boost with Few Short Compound Activity

An Effective Stroke Prediction Based on Cat Boost with Few S...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Rana Veer Samara Sihman Brahmatej Rupavath Ramy R. Hussein Mohan M Vasanthakumar G U S. Subburam Indiana Wesleyan University Marion USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering K P R Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Information Technology New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

In recent years, strokes are becoming a maj or global health concern that affecting the welfare of the population. Many researches have focused on developing predictive models using Machine Learning (ML) and Deep Learning (DL) for strokes. However, the existing method Support Vector Machine (SVM) used to predict the strokes but struggled with sensitivity issues. To overcome this issue, Cat Boost with Few-Shot Compound Activity Prediction (CB-FSCAP) is proposed for stroke prediction. The Few-Shot Compound Activity Prediction (FSCAP) effectively learns the representation from labeled data to reduce the sensitivity issues. Primarily, the input data is taken from healthcare-dataset-stroke-data dataset and the further processed into preprocessing. The stop words are removed and imputation, lemmatization are utilized to reduce dimensionality in preprocessing. Then, the preprocessed data is fed into Cat Boost (CB) for selecting the optimal features. Finally, prediction is done with the help of FSCAP to predict the strokes in various emergency conditions. From the results, the Proposed CB-FSCAP established better results when compared to existing Random Forest (RF) by providing results in terms of accuracy (98.4%), precision (95.1 %), recall (96.50/0) and F1-score (95.7%) respectively.

关键词： Support vector machines Radio frequency Sensitivity Accuracy Transforms Predictive models Imputation Power capacitors Compounds Random forests

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Anomaly Detection in Social Networks using Improved Local Outlier Factor

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

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Rana Veer Samara Sihman Bharattej R Muntader Mhsnhasan Sowmya M Prabhakaran M V K. Ghamya Department of Computer Science Saint Leo University FL USA Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Artificial Intelligence and Data Science Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering (Cyber Security) Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

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.

关键词： Measurement Accuracy Social networking (online) High dimensional data Transfer learning Feature extraction Real-time systems Intelligent systems Anomaly detection Principal component analysis

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