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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Khan, Muhammad Attique Mir, Momina Ullah, Muhammad Sami Hamza, Ameer Jabeen, Kiran Gupta, Deepak Department of Computer Science HITEC University Taxila47080 Pakistan Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Arunachal Pradesh Jote710003 India

ISBN: (纸本)9789819708918

One of the most serious illnesses in the world is oral cancer, and prompt, effective treatment can significantly increase patient survival and cure rates. The manual diagnosis from Histopathologic Images (HI) is time-consuming and requires an expert doctor. To reduce the burden on a doctor, a computerized technique is helpful for the classification of medical image analysis. In this work, we proposed a deep learning framework for classifying Oral Squamous Cell Carcinoma (OSCC) from HI. Two pre-trained deep learning architectures, MobileNet-V2 and DarkNet-19, have been fine-tuned in the proposed framework. Both architectures have been selected based on recent performance and fewer parameters. Both models were trained using the transfer learning concept and extracted features from the global average pooling layers. A Chaotic Crow Search optimization algorithm has been employed, and the best features have been selected. The selected features are finally classified using machine learning classifiers. A publicly available dataset was utilized for experimental purposes and obtained the highest accuracy of 92%. Compared with some state-of-the-art techniques, the proposed framework shows improved accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad Image Processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. Pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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Knowledge-Driven Possibilistic Clustering with Automatic Cluster Elimination

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computers, Materials & Continua 2024年第9期80卷 4917-4945页

作者： Xianghui Hu Yiming Tang Witold Pedrycz Jiuchuan Jiang Yichuan Jiang The School of Computer Science and Engineering Southeast UniversityNanjing211189China The School of Computer and Information Hefei University of TechnologyHefei230601China The Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada The Systems Research Institute Polish Academy of SciencesWarsaw00-901Poland The School of Information Engineering Nanjing University of Finance and EconomicsNanjing210023China

Traditional Fuzzy C-Means(FCM)and Possibilistic C-Means(PCM)clustering algorithms are data-driven,and their objective function minimization process is based on the available numeric ***,knowledge hints have been introduced to formknowledge-driven clustering algorithms,which reveal a data structure that considers not only the relationships between data but also the compatibility with knowledge ***,these algorithms cannot produce the optimal number of clusters by the clustering algorithm itself;they require the assistance of evaluation ***,knowledge hints are usually used as part of the data structure(directly replacing some clustering centers),which severely limits the flexibility of the algorithm and can lead to *** solve this problem,this study designs a newknowledge-driven clustering algorithmcalled the PCM clusteringwith High-density Points(HP-PCM),in which domain knowledge is represented in the form of so-called high-density ***,a newdatadensitycalculation function is *** Density Knowledge Points Extraction(DKPE)method is established to filter out high-density points from the dataset to form knowledge ***,these hints are incorporated into the PCM objective function so that the clustering algorithm is guided by high-density points to discover the natural data ***,the initial number of clusters is set to be greater than the true one based on the number of knowledge ***,the HP-PCM algorithm automatically determines the final number of clusters during the clustering process by considering the cluster elimination *** experimental studies,including some comparative analyses,the results highlight the effectiveness of the proposed algorithm,such as the increased success rate in clustering,the ability to determine the optimal cluster number,and the faster convergence speed.

关键词： Fuzzy C-Means(FCM) possibilistic clustering optimal number of clusters knowledge-driven machine learning fuzzy logic

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Optimizing Database Systems for Parallel Processing in Multi-core Environments 6th

Optimizing Database Systems for Parallel Processing in Multi...

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6th International Conference on Information Systems and Management Science, ISMS 2023

作者： Mallegowda, M. Alex, Sini Anna Srivastava, Sajal Singh, Shashank kanavalli, Anita Department of Computer Science and Engineering Ramaiah Institute of Technology Karnataka Bangalore560054 India Ramaiah Institute of Technology Karnataka Bangalore560054 India

ISBN: (纸本)9783031707889

In contemporary times, parallel database systems have gained widespread acceptance, finding applications in both online transaction processing (OLTP) servers and online analytical processing (OLAP) servers. These advancements encompass the realms of parallel database processing and querying. A pivotal technique in enhancing the efficiency of parallel database systems, particularly in the context of data warehousing, is parallel query optimization. When presented with an SQL query, parallel query optimization diligently explores parallel plans to identify the one that yields the query result most swiftly. The focal point of this research article revolves around introducing an innovative strategy for parallelizing the execution of multithreaded queries in relational databases. The objective is to optimize the utilization of CPU and memory resources to their fullest potential. This approach adeptly addresses the challenge of reducing response time for concurrent database queries, thereby significantly enhancing their efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Structured Query Language

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A Real-Time Road Crash Prediction Model by Hybridizing Multiple Learning Classifiers 4th

A Real-Time Road Crash Prediction Model by Hybridizing Multi...

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4th International Conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Arun, G. Anuguraju, K. Sangeetha, A. Babu, K. Trichy India Department of Computer Science and Engineering Gnanamani College of Technology Pachal Namakkal India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9789819914784

The majority of critical injuries caused by road accidents occur in developing nations, where they are a major global concern. Due to these traffic incidents, many individuals have lost loved ones. Consequently, a system that has the potential to save lives is needed. The approach identifies key contributing factors for accidents or establishes a connection between accidents and different causes of accidents. This study suggests a system for predicting accidents that can assist in analyzing potential safety concerns and foretelling whether an event will happen or not. In order to identify key qualities and forecast accident risk in single- and multiple-crashes, this research provides a hybrid feature selection-based classification-based machine learning (ML) method. It took into account classifiers like the random forest, k-nearest neighbor, and XGBoost. A few evaluation criteria, including precision, root mean square error, accuracy, recalls, and receivers operating output characteristics, were used to assess these classifiers. When the accuracy of the ML algorithms was compared, the accuracy of the proposed model attained as 99.78%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Biomass Fuel Classification from Agricultural Residues Using Machine Learning 12

Biomass Fuel Classification from Agricultural Residues Using...

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12th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2024

作者： Pachauri, Nikhil Gupta, Himanshu Verma, Om Prakash Kumar Singh, Sushil Verma, Deepak Rn, Ravikumar Manipal Academy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal Institute of Technology Manipal Academy of Higher Education Department of Instrumentation and Control Engineering Karnataka Manipal India Dr B R Ambedkar National Institute of Technology Jalandhar Department of Instrumentation and Control Engineering Jalandhar India Marwadi University Department of Computer Engineering of Computer Engineering Gujarat Rajkot India Marwadi University Dept. of Computer Engineering Gujarat Rajkot India

ISBN: (纸本)9798350340204

This work incorporates an adaptive learning-based boosting (ADboost) ML classifier to classify four types of fuel: agricultural residue, coals, wood, and produced biomass. Further, the ADboost's hyperparameters, such as learning rate, maximum number of splits, and minimum leaf size are adjusted using teaching learning-based optimization (TLBO), resulting in TADboost. The performance of TADboost is compared against various popular ML (NN, BAG, NB, and SVM) models. Simulated result reveals that the suggested classifiers outperform other compared ML classifiers for fuel classification with the classification accuracy, precision, recall, F1-score, and kappa as 0.9659, 0.9671, 0.9449, 0.9558, and 0.9482, respectively. © 2024 IEEE.

关键词： Adversarial machine learning

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Holistic Knowledge Framework for Improving PC Design using Big Data Analytics 7

Holistic Knowledge Framework for Improving PC Design using B...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Rajendran, T. Vignesh, O. Priyadharsini, V. Jagadeesh, K. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Easwari Engineering College Department of Electronics and Communication Engineering Chennai India Akshaya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India GRT Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350397253

Cyberspace is massively expanding every day, and the users of these digital devices are looking for more innovative applications to ease their day-to-day work. The main objective of any device is to use available system resources at a maximum optimum level and give high performance to the users. Especially in personal computers (PC), there is still a massive gap in the user experience and utilization of available system resources. There are several situations where error conditions and system responsiveness lag. Over several years the digital world evolved and provided high-performance computing. Furthermore, the PC design enhancements could be carryout through the participation of PC users. That could be accomplished by collecting the system performance indicators, system behavior and properties, software application usage, user working log, and other wide range of real-time data from the PC. These telemetry data needed to be retrieved from an extensive array of PCs then these datasets would be reposited on the centralized servers. By applying the big data analytical methods and other mining algorithms over these extensive datasets to identify the hidden patterns and discover more knowledge about the key indicators that would help improve PC performance and user experiences. Through this knowledge acquisition model, the organizations and research institutions could develop the most resilient and adoptable PC designs and software applications developments. © 2023 IEEE.

关键词： Personal computers

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Empowering Women's Safety: Artificial Intelligence and IoT-Enabled Smart Wearable Device for Real-Time Monitoring and Emergency Assistance 8

Empowering Women's Safety: Artificial Intelligence and IoT-E...

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Clement, Jayapratha Ramya, R. Gomathi, T. Surendran, R. Kalyani, G. Karpaga Vinayaga College of Engineering and Technology Department of Computer science and engineering Tamil Nadu Chengalpattu India Chennai Institute of Technology Department of Biomedical Engineering Chennai India Sathyabama Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India RuralCare Innovators LLP Tamil Nadu Madurai India

ISBN: (纸本)9798331520762

Women's safety remains a pressing concern worldwide, necessitating multifaceted approaches to address physical, emotional, and social threats. Despite strides in gender equality, women still face significant risks of violence and harassment in diverse settings, from homes and workplaces to public spaces. It is estimated that globally, 137 women are killed each day by an intimate partner or a family member. Furthermore, in the United States, 19.3% of women have experienced rape at some point in their lives, and 43.9% have been subjected to sexual violence other than rape. Technological innovations, like safety apps and improved public transport security, offer additional protective layers. Many safety apps and devices are inaccessible due to cost, lack of awareness, or technological literacy, particularly in underprivileged or rural areas. Our aim is to create a system that will help make every location and every minute safer for women. The goal of this invention is to design an Android app that women may use in any type of scenario in day-to-day life. The proposed Android-based application will subsequently update the user's position from dawn to evening. When an individual crosses the geographical region, an SMS alert is delivered to the selected contact already updated in the mobile app and also send an alert when the user vital signs exceeds threshold values. © 2025 IEEE.

关键词： Emergency services

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Roselinkiruba, R. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to increase the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three important criteria such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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