检索结果-内蒙古大学图书馆

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： P ShunmugaLakshmi V Ram Kishore Pillai Pratham Ramachandran S Santhi S Kalaiselvi B.Tech Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Information Technology National Engineering College Kovilpatti Tamil Nadu Dept. of Computer Science and Engineering National Engineering College Kovilpatti Tamil Nadu

In order to develop strategic initiatives in agricultural belonging for trading protocol and tripling countrymen earnings, early and accurate agricultural output estimation is pivotal in computable and commercial decision at the territory plane. Crop production forecasting, which is done to foresee a greater crop output, is the impressive difficulty in the agronomic division. To help farmers increase agricultural yield, this study has collected and evaluated data on N, P, K, temperature, humidity, ph, rainfall and soil_moisture. The superior values of this work are to recommend the crop to cultivate, suggest the appropriate fertilizers to use and predict the disease of a crop. A python environment is used to perform initial preprocessing on the data. KNN classification is used for developing the recommendation system. The link between the count, nutrients, temperature, humidity, pH, rainfall, and crop, are visualized using bar graphs and scatter plots. The tensor and torch were used to forecast crop disease and also to suggest appropriate fertilizer based on the data. The application was developed using Python Flask. This Classification was compared with other algorithms such as SVM, DT , RF and K-means.

关键词： Support vector machines Temperature sensors Linear regression Crops Humidity Prediction algorithms Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Real-Time Video Enhancement with Spatio-Temporal Attention via Deep Convolutional Neural Networks 3rd

Real-Time Video Enhancement with Spatio-Temporal Attention v...

引用

3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Maria, Anto bennet balasubramanian, Swaminathan Vijayan, Rajmohan Sonthi, Vijaya Krishna Ramakrishnan, Jayapratha Manogaran, Umamaheswari Sengan, Sudhakar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Karnataka Bengaluru560069 India Tamil Nadu Chennai602107 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The realm of video content has witnessed exponential growth, and alongside it emerges the challenge of enhancing video quality efficiently. Traditional techniques, although valuable, often fall short of addressing the intricate details and temporal consistencies required for high-quality video upscaling. Moreover, real-time processing demands methods ensuring accuracy and swift computational speeds. Addressing these challenges, this paper introduces the STA-DCNN. This innovative architecture is rooted in combining spatial and temporal attention mechanisms to produce superior video super-resolution. The Spatio-Temporal Attention Module (STAM) acts as a cornerstone, singling out essential features across frames, while the Deep Convolutional backbone (DCb) intensifies these frames, capitalizing on attention-informed features. Furthermore, the Temporal Memory Unit (TMU) preserves crucial aspects from preceding frames, guaranteeing a seamless video rendition. Empirical tests confirm that STA-DCNN notably surpasses its counterparts, offering an ideal balance of quality and real-time processing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

Individual Privacy Protection in Cybersecurity Using Data Analytics and Machine Learning

Individual Privacy Protection in Cybersecurity Using Data An...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Arun, M. bhavadharani, K. Supreeth, b.R. Pundir, Sumit Pradeepa, K. Razak, K. Abdul Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Tamil Nadu Karur India Oxford Degree and Pg College Department of Computer Science Sir. M. VisveshwaraiahLayout Ullal Bangalore India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India K. Ramakrishnan College of Engineering Department of Mathematics Tamil Nadu Trichy India

ISBN: (纸本)9798331543617

Traditional cybersecurity addresses struggle to keep up with the ever-changing nature of cyber threats and usually fail to detect new privacy breaches. The paper proposes a proactive protection mechanism that employs data analytics and machine learning (ML) to promote individual privacy in the digital realm. The system analyzes several data sources, including system events, network traffic, and user activity logs, using advanced ML methods like Random Forests (RF), Support Vector Machines (SVM), and Neural Networks (NN), to detect and prevent privacy violations. Ensemble learning techniques, feature engineering, and real-time monitoring enable adaptability in the face of shifting threats. According to the results, the proposed system surpassed the existing system in terms of accuracy (0.92), recall (0.89), precision (0.91), and F1 score (0.90). Its ability to detect unlawful access, unusual logins, and irregular data requests is proved by anomaly detection rates utilizing One-Class SVMs (91%) and Isolation Forest (89%). The system's low false positive rate of 5% demonstrates how effectively it can detect privacy breaches, resulting in stronger cybersecurity and greater privacy protection for both individuals and enterprises. © 2024 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Recommendation of Personalized Learning Path in Smart E-learning Platform Using Reinforcement Learning Algorithms 8th

Recommendation of Personalized Learning Path in Smart E-lear...

引用

8th International Conference on Inventive Communication and Computational technologies, ICICCT 2024

作者： Deepa, S. Arunkumar, M.S. Kanimozhi, T. Eswaran, balamurugan Duraivelu, Vivek Sweatha, M. Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Erode Perundurai638060 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Science and Engineering Bannariamman Institute of Technology Sathyamangalam India School of Information Technology Texila American University Lilayi Zambia Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Narsapur502313 India Kongu Engineering College Tamil Nadu Erode Perundurai638060 India

ISBN: (纸本)9789819777099

The exponential expansion of e-learning has significantly broadened educational opportunities globally, empowering students to engage in learning experiences from anywhere. However, the abundance of online courses poses a challenge in customization, often resulting in diminished learning outcomes as learners struggle to select suitable courses tailored to their unique requirements. Personalized learning addresses this by adapting activities to individual needs, fostering the development of capabilities and personality. The proposed system integrates Markov Decision Processes (MDPs) and Reinforcement Learning (RL), leveraging deep Q-learning for sequential recommendations and MDP adaptability to tailor learning paths. This combined approach holds promise for revolutionizing e-learning by delivering personalized, adaptive experiences. Learners benefit from tailored courses, activities, and learning paths, optimizing their educational journey. Continuous adaptation and improvement, facilitated by the core MDP framework and user feedback, further enhance effectiveness in e-learning environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Prevention from gas devastation

Prevention from gas devastation

引用

2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Datta, Sanghita Sah, Ankita Gautam, Sidharth Das, Upamita Maity, Saikat B. Tech Computer Science Engineering JIS University India

LPG leakages are the most common issues found in household and gas plants. This issue is life threatening not only for the humans in the vicinity but also for the surrounding areas. The idea behind our project is to save the lives of those innocents who can get affected due to the carelessness of certain people. So, as soon as the gas gets detected by the system, the concerned people around will receive messages as well as the alarm rings. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Gas plants

来源：评论

学校读者我要写书评

暂无评论

An IoT and Random Forest-DT-based Early Enhanced Forest Fire Detection in Large and Small Targets 3

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

引用

3rd International Conference for Innovation in technology, INOCON 2024

作者： Prabakaran, Manjula Wadhwani, Omesh Pandiaraj, V. Zainab, Kaneez Nishant, Neerav Justin, Z. Andhra Pradesh India Shri Ramdeobaba College of Engineering and Management Department of Information Technology Nagpur India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mechanical Engineering Chennai India B.N. College of Engineering and Technology Department of Computer Science & Engineering Lucknow India Babu Banarasi das University School of Engineering Department of CSE Lucknow India Thiruvalluvar India

ISBN: (纸本)9798350381931

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth's climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LbP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach. © 2024 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Detection of Customer Interested Garments in Surveillance Video using computer Vision

arXiv

引用

arXiv 2025年

作者： Ijjina, Earnest Paul Joshi, Aniruddha Srinivas Kanahasabai, Goutham Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India B.Tech. Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India

One of the basic requirements of humans is clothing and this approach aims to identify the garments selected by customer during shopping, from surveillance video. The existing approaches to detect garments were developed on western wear using datasets of western clothing. They do not address Indian garments due to the increased complexity. In this work, we propose a computer vision based framework to address this problem through video surveillance. The proposed framework uses the Mixture of Gaussians background subtraction algorithm to identify the foreground present in a video frame. The visual information present in this foreground is analysed using computer vision techniques such as image segmentation to detect the various garments, the customer is interested in. The framework was tested on a dataset, that comprises of CCTV videos from a garments store. When presented with raw surveillance footage, the proposed framework demonstrated its effectiveness in detecting the interest of customer in choosing their garments by achieving a high precision and recall. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

An Advanced Encryption Algorithm for Enhancing Data Security in Cloud Computing

引用

Recent Advances in Electrical and Electronic engineering 2025年第5期18卷 623-636页

作者： Anandh, R. Swaminathan, A. Jadhav, Swati Dhondiram Valarmathi, P. Gopinath, N. Tiwari, Kanchan S. Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai India Department of Computer Engineering Keystone School of Engineering Maharashtra Pune India Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Telecommunication Modern Education Society’s Wadia College of Engineering Pune India

Aim: Cloud computing (CC) is a revolutionary new archetype in which users pool their computing resources to provide greater efficiency for everyone. Data become increasingly vulnerable to diverse security threats from attackers when millions of users circulate the same network for data transmission. Protecting these reports has shifted to the vanguard of priorities. The existing data security approach prioritizes protecting data at rest in cloud storage but gives less thought to protecting data in transit. During transmission, the data are vulnerable to intrusion attempts. Methods: The third-party auditor is provided access to data during the transfer phase, which is also the current pattern. As the attacker can now pose as a trusted third party, it makes the data more susceptible to unauthorized access. However, growing concerns regarding data privacy and security have made outsourcing sensitive information to faraway data centers difficult. As a result, new security concerns in the cloud necessitate an improved version of the tried-and-true advanced encryption standard (AES) algorithm. Key aspects presented in this study include a secure and private framework for owner data. It improves upon the 128 AES technique by adding a second round of encryption using a different key, allowing for a throughput of 1000 blocks per second. However, the standard method uses a single round key and only 800 blocks per second. The suggested approach reduces energy consumption, improves load distribution, and optimizes network trust and resource management. Results: The proposed architecture allows for the use of AES with cipher lengths of 16, 32, 64, and 128 bytes. The effectiveness of the algorithm in terms of attaining target quality metrics is illustrated graphically via simulation results. This strategy reduces power consumption by 13.23%, network utilization by 12.43%, and delay by 16.53%, according to the outcomes. Conclusion: As a result, the recommended architecture enhance

关键词： Sensitive data

来源：评论

学校读者我要写书评

暂无评论

Twitter sentiments analysis using machine learninig methods

Twitter sentiments analysis using machine learninig methods

引用

2020 International Conference for Emerging technology, INCET 2020

作者： Mandloi, Lokesh Patel, Ruchi Meidcaps University B.Tech Computer Science and Engineering Indore India Meidcaps University Dept. Computer Science and Engineering Indore India

ISBN: (纸本)9781728162218

Analysis of sentiments is the method of deciding whether the sentiments in the text is positive, negative or neutral. It is also known as material polarity or mining of opinions. The growth and advancement in social media platforms engaged a huge number of users. Social media platform like twitter where users can post their tweets in 280 characters. because of the limited number of characters in tweets, it becomes easy for the sentiment analysis. On Twitter 550 millions of tweets are posted daily. Twitter also represents all age group people and also a fair representation of gender. Therefore, the sentiment analysis of twitter data becomes somewhat general sentiments of society. In this paper, we will compare various Machine Learning methods like the Naïve bayes Classification method, Support Vector Machine Classification Method and Maximum Entropy Classification method. We will see how sentiments analysis is done by this classification algorithm and what is the accuracy and precision in these cases. © 2020 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Feline-Inspired Optimizer Enhanced Through Self-Improvement, Coupled With Machine Learning, For the Identification Of Lung Cancer In CT Scans 2

A Feline-Inspired Optimizer Enhanced Through Self-Improvemen...

引用

2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Geetha, S. Jose, Naduvathezhath Nessariose Vivekanandan, G. Shrutika Shree, Sowmya H.S. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu600062 India Department of Computer Science and Engineering V.S.B. Engineering College Karur India Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai India Department of Computer Science University of southern California Department Bangalore Institute of Technology K R Road V V Pura Karnataka Bengaluru560004 India

ISBN: (纸本)9798350383867

The study introduces an innovative breakthrough in the analysis of medical images, particularly focusing on the timely identification and categorization of lung cancer using computed tomography (CT) scans. Employing the pioneering Cat Mouse Optimizer algorithm, which has undergone enhancements via self-improving mechanisms, we' ve crafted an intricate framework. This framework seamlessly integrates cutting-edge machine learning algorithms to streamline the process of diagnosing lung cancer from CT scans with unparalleled precision and efficiency. Conventional methods for diagnosing lung cancer often hinge on manual interpretation of CT images by radiologists, a process susceptible to time constraints and human error. Our methodology tackles these challenges by capitalizing on the capabilities of artificial intelligence and deep learning methodologies. Through extensive training on extensive datasets of annotated CT scans, our model becomes adept at identifying nuanced patterns and indicators of lung cancer, leading to robust classification outcomes. The hallmark attributes of our system encompass its flexibility and scalability, enabling continual enhancement and adaptation over time. The self-enhancing features of the Cat Mouse Optimizer algorithm empower the system to autonomously fine-tune its algorithms and parameters, informed by feedback from new data and user interactions. This guarantees that the system remains abreast of the latest advancements in medical imaging and lung cancer research. Moreover, our framework underscores the importance of interpretability and transparency, furnishing clinicians with invaluable insights into the decision-making process of the AI model. This cultivates trust and collaboration between human experts and AI systems, culminating in more precise diagnoses and enhanced patient outcomes. Through rigorous experimentation and validation across varied datasets, we showcase the effectiveness and dependability of our methodology in re

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：