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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Batra, Raman Kumar, Sanjeev Bargavi, Manju Noida Institute of Engineering and Technology Department of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The integration of edge computing is critical to the development of Beyond 5G (B5G) and 6G networks, as the volume of data and processing demands continue to rise. Using the radio access network (RAN), mobile edge computing (MEC) is essential for putting advanced cloud computing features in close proximity to end users. Even though MEC and its possible applications have been thoroughly studied, there hasn't been much conversation about the crucial elements required for MEC to be deployed successfully in order to satisfy various application demands. The important considerations that must be made while establishing edge infrastructure in B5G and 6G networks are outlined in this article. These factors include things like user density, server location, capacity, edge architecture, and security protocols. Moreover, it includes concrete assessments of edge computing-dependent applications, such forecasting and video conversion using deep learning. Additionally, the study provides insightful viewpoints that clarify open research questions related to edge computing within the framework of 6G networks. © 2023 IEEE.

关键词： and Cloud computing Distributed computing Edge processing Next-generation 6G communication networks

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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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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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Security Attacks and Key Challenges in Blockchain technology: A Survey 4th

Security Attacks and Key Challenges in Blockchain Technology...

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4th International Conference on Advances in Information Communication technology and Computing, AICTC 2022

作者： Patel, Rima Patel, Dharmendra Department of Computer Science & Engineering Faculty of Technology & Engineering Devang Patel Institute of Advance Technology and Research CHARUSAT Changa India Department of Computer Application Smt. Chandaben Mohanbhai Patel Institute of Computer Applications CHARUSAT Changa India

ISBN: (纸本)9789811998874

A blockchain is decentralized, distributed and public ledger, which is used to record the transaction across the number of clusters of computers. The record cannot be altered without an alternation in subsequent blocks. In the world of IT, the term Blockchain is not obscure. Despite tremendous applications in the field of information technology, finance and other industries, there are some challenges with Blockchain technology that need to be taken into consideration. In this paper, we have discussed the key challenges and issues with Blockchain technology. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning

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Neural Computing and Applications 2024年第33期36卷 20723-20750页

作者： Abdel-salam, Mahmoud Kumar, Neeraj Mahajan, Shubham Faculty of Computer and Information Science Mansoura University Mansoura Egypt Department of CSE Thapar Institute of Engineering and Technology Deemed to Be University Patiala147004 India Delhi-NCR India

Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. © The Author(s) 2024.

关键词： Support vector regression

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A Fusion Framework of Pre-trained Deep Learning Models for Oral Squamous Cell Carcinoma Classification 3rd

A Fusion Framework of Pre-trained Deep Learning Models for O...

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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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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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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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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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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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