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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Prabith, G.S. Rohit Narayanan, M. Arya, A. Aneesh Nadh, R. Binu, P.K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

In this research paper, a detailed investigation presents the utilization of the BiT5 Bidirectional NLP model for detecting vulnerabilities within codebases. The study addresses the pressing need for techniques enhancing software security by effectively identifying vulnerabilities. Methodologically, the paper introduces BiT5, specifically designed for code analysis and vulnerability detection, encompassing dataset collection, preprocessing steps, and model fine-tuning. The key findings underscore BiT5's efficacy in pinpointing vulnerabilities within code snippets, notably reducing both false positives and false negatives. This research contributes by offering a methodology for leveraging BiT5 in vulnerability detection, thus significantly bolstering software security and mitigating risks associated with code vulnerabilities. © 2024 The Authors. Published by Elsevier B.V.

关键词： Natural language processing systems

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Surveying Keyword Extractors: Classification, applications, and Empirical Analysis 6

Surveying Keyword Extractors: Classification, Applications, ...

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6th Parul International Conference on Engineering and Technology, PICET 2024

作者： Musunuru, Varun Nagam, Sai Pavan Krishna Thushara, M.G. Villuri, Praneeth Vasantha, Gopikrishna Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350369748

Natural Language Processing (NLP) has seen some rapid developments. One of the areas is keyword Extraction which has seen rapid growth. Many varied and complex extractors have been brought to light recently, and several extractors have been upgraded. These can be used in several NLP applications. The analysis and working of some of the newest and benchmark extractors that shed new light in this field have been discussed. The goodness of any extractor can only be measured by how it works on real-life data. Instead of going down the set route, a detour was taken, and the working of keyword extractors on movie plots was observed and discussed. Thus for the analysis part, a new dataset has been created from several movie plots taken from various credible sources like IMDB, Rotten Tomatoes, etc. © 2024 IEEE.

关键词： Natural language processing systems

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'Optimizing Algorithmic Efficiency: Exploring Advanced Data Structures for High-Performance Computing' 3

'Optimizing Algorithmic Efficiency: Exploring Advanced Data ...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Rao, Lade Gunakar Nitya, Erupaka Devi Kusumba, Jyoshna Rajchandar, K. Narasimha Murthy, S. SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Telangana Hyderabad India Government Degree College Computer Science And Applications Telangana Warangal India Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350361391

This work explores advanced data structures as a means of optimizing algorithmic efficiency in high-performance computing. The search for faster and more scalable algorithms becomes essential as computing demands rise further. This paper explores state-of-the-art data structures intended to improve overall performance, memory usage, and computational speed. We hope to offer insights into the applicability and impact of various advanced data structures on high-performance computing tasks through a thorough review and analysis of them, including but not limited to self-balancing trees, hash tables, and advanced graph structures. The results of this study not only advance our theoretical knowledge of these data structures but also provide developers and researchers with useful advice on how to utilize algorithmic efficiency in quickly fully. It is important to remember that algorithmic urban planning is an interdisciplinary field that encompasses data science, computer science, urban planning, and many other fields. The goal of integrating algorithms and smart technologies is to build more efficient, sustainable, and liveable urban environments.[8]. © 2024 IEEE.

关键词： Advanced Data Structures Algorithmic Efficiency Computing (keywords) High-Performance Computing Optimizing

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From Monochrome to Technicolor: A Comprehensive Review of Video Colorization 25

From Monochrome to Technicolor: A Comprehensive Review of Vi...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Aydam, Zahoor M. El Abbadi, Nidhal K. University of Kufa Faculty of Computer Science and Mathematics Computer Science Department Najaf Iraq Al-Mustaqbal University Al-Mustaqbal Center for Ai Research and Applications Babylon Iraq

ISBN: (纸本)9798331540012

Video colorization is the process of adding colors to black and white video. This task is a compelling problem and intriguing challenge in image processing. With the emergence of deep learning and the availability of a wide range of datasets, significant developments have been made in video colorization algorithms. However, this task presents unique challenges, particularly in ensuring temporal coherence and maintaining spatiotemporal consistency across frames. In this work, we aim to offer a comprehensive overview of state-of-the-art techniques in video colorization, examining the challenges, progress in current techniques, and future direction, by analyzing many papers and research. The goal of this paper is to provide the researchers with a piece of valuable information about video colorization. © 2024 IEEE.

关键词： Video analysis

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The Performance Analysis and Comparison of Azure and AWS Hypervisor using Different Workloads in Virtualization 7

The Performance Analysis and Comparison of Azure and AWS Hyp...

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

作者： Poovizhi, J Mary Ramya Devi, R. Schools of Computing Sciences Department of Computer Science Chennai India Schools of Computing Sciences Department of Computer Applications Chennai India

ISBN: (纸本)9798350397253

The abstraction of IT infrastructure enables the integration and pooling of IT resources to be shared across several applications to compensate for declining resources. Growing business needs virtualization offers a conceptual encapsulation of current computing resources. It generates computer systems unrestricted by physical design. The implementation objective of virtualization in cloud computing is to simplify the provision of products by providing a platform for maximizing complex IT resources in a scalable way, hence decreasing the cost of cloud computing. Hypervisors are one of the essential components of the virtualization of Hardware. Many operating systems can run simultaneously on a single physical server in a virtualized environment. For cloud computing, the hypervisor is the best way to deliver a variety of operational environments. This work will compare the cloud centre hypervisor Azure and AWS with different environments and workloads. The result shows not all hypervisors will provide the same energy and power conception with workloads and environments. © 2023 IEEE.

关键词： Energy efficiency

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Two-Stage Model for Copy-Move Forgery Detection 1st

Two-Stage Model for Copy-Move Forgery Detection

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1st International Conference on Computational Intelligence for Engineering and Management applications, CIEMA 2022

作者： Kumari, Ritesh Garg, Hitendra Chawla, Sunil Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Chandigarh University Mohali India

ISBN: (纸本)9789811984921

Digital images can be tempered or modified very easily with many tools available on the internet. Nowadays some software not only enhance and retouch but also copy-move objects of the image in such a way that is hard to differentiate. Image forgery detection deals with the recognition of tempering like splicing, copy-move, and other modifications done on the image. One of the most common ways of passive forgery detection is the block-based copy-move technique. In this paper, forgery detection is achieved in two parts. In the first part, Copy-Move forgery is classified by support vector machine (SVM) and features are extracted by local binary pattern (LBP) and discrete cosine transform (DCT). However, the region of forgery is calculated in the second part with the help of the Gray-level Co-occurrence Matrix (GLCM) through overlapping blocks. The whole experiment and evaluations are performed on the CoMoFoD image data set. The performance is measured in terms of accuracy and sensitivity. The promising result indicates the effectiveness of the model with enhanced accuracy of 97.82%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Local binary pattern

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An Analysis of Irrigation Management for Crops using Machine Learning Algorithms 1

An Analysis of Irrigation Management for Crops using Machine...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Thamilselvan, R. Natesan, P. Rajalaxmi, R.R. Prasanth, D. Nithish Swaminathan, S. Yuvapriya, R. Kongu Engineering College Computer Science and Engineering Perundurai Erode India Kongu Engineering College Department of Computer Applications Perundurai638 060 India

ISBN: (纸本)9798350372748

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. Machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector Machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Impact of nutrients in food quality and safety by machine learning classifier using internet of things

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International Journal of Information Technology (Singapore) 2024年第5期16卷 2803-2812页

作者： Balamurugan, S. Gurumoorthi, E. Devi, P.P. Maruthamuthu, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp Department of IT CMR College of Engineering and Technology Telangana Hyderabad India Department of Computer Applications Madanapalle Institute of Technology and Science AP Madanapalle India

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Bayes classifier algorithm Food safety and IoT FSC Traceability Tree augmented Naive Bayes

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

Glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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Research on Home Energy Consumption Optimization Based on User Habit Analysis

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 839-855页

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Lin, Tao Kong, Lingping College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science VSB-Technical University of Ostrava Czech Republic

With the development of smart electricity technology and demand response, optimization of household electricity consumption behavior has become an important research element for energy saving in residential buildings. In the study of smart electricity consumption in households, the differences in users’ lifestyles and their preferences for the use of various appliances can have a great impact on the results. And many existing methods need to rely on users’ awareness, which does not meet the popular demand. In this paper, we propose a new method for residential load scheduling that takes into account the load characteristics of appliances and electricity consumption habits. By analyzing the household electricity consumption data set and mining the personalized needs and us-age preferences of this user for various appliances, we establish an optimization model for electricity consumption behavior that combines the minimization of electricity expenses and user comfort. Finally, an improved artificial bee colony algorithm is proposed for solving the optimization model and generating a personalized dispatching strategy combined with real-time electricity pricing (RTEP) tariff. The proposed improved artificial swarm algorithm is compared with other classical algorithms, including GA, PSO, ABC, and QABC, and the analysis of cases shows that the model can effectively reduce the electricity consumption cost and ensure the customer satisfaction, and the proposed improved ABC-based algorithm outperforms other algorithms in terms of cost and user comfort. © 2023, Journal of Network Intelligence.

关键词： Customer satisfaction

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