检索结果-内蒙古大学图书馆

Cyber Security Challenges and Effective Security Measures for IOT-based Intelligent Healthcare Systems

Recent Advances in Electrical and Electronic engineering 2025年第6期18卷 735-747页

作者： Somnathe, Abhijit T. Gopinath, N. Deena, G. Anandh, R. Kumar, Santosh Tiwari, Kanchan S. Electronics and Telecommunication Smt Kashibai Navale College of Engineering Savitribai Phule Pune University Pune India Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Electrical and Electronics Engineering Gaya College of Engineering Bihar India Electronics and Telecommunication Modern Education Society’s Wadia College of Engineering Pune India

Aims and background: In order to keep up with the rapid development of technology, cities must enhance their offerings in the areas of public security, medical care, transportation, and citizen well-being. Healthcare systems rely heavily on patient care. Patients' life and the services offered by doctors, nurses, clinicians, pharmaceutical companies, and governments stand to benefit from the implementation of Healthcare IoT. There has been a change in the medical industry, marked by the widespread adoption of wireless healthcare-monitoring-systems (HMS) in hospitals. Methodology: However, the Internet-of-Things (IoT) concept frequently ignores security-privacy for linked things. Security and privacy protections must be implemented systematically throughout the entire healthcare and remote health monitoring system, from the creation of equipment to their interconnection, communication, storage, and eventual destruction. This research investigates the risks associated with using smart health devices and provides recommendations for addressing those risks. The present security and privacy of IoT devices in healthcare systems, as well as the difficulties in adopting security frameworks, are discussed, and recommendations for addressing these issues are offered. Results: This study also contributes in two ways to the ongoing effort to investigate securityprivacy concerns in the circumstance of smart cities for healthcare purposes. Conclusion: Moreover, this article provides a summary of the many Internet of Things applications and the cyber threats they face. On the other hand, thorough assessments of methods to reduce cyber threats to 60% are presented. © 2025 bentham Science Publishers.

关键词： Cyber attacks

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

Innovative techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits 2

Innovative Techniques in the Construction of Very Large Scal...

引用

2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Saravanan, V. Sasikala, G. Gomathi, P. Kirubakaran, D. Rajalingam, A. Pagunuran, Jubert R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India V.S.B. Engineering College Department of Ece India R.M.K Engineering College Department of Computer Science and Engineering India University of Technology and Applied Sciences College of Engineering and Technology Engineering Department Shinas Oman

ISBN: (纸本)9798350388916

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems. © 2024 IEEE.

关键词： VLSI circuits

来源：评论

学校读者我要写书评

暂无评论

Design and Development of Hybrid Feature Selection Method for Classification

Design and Development of Hybrid Feature Selection Method fo...

引用

2021 International Conference on Intelligent technologies, CONIT 2021

作者： Sreedevi, M. Manasa, Garikapati Apurva, Idupulapati Koneru Lakshmaiah Education FoundationVaddeswaram Dept of Computer Science and Engineering Andhra Pradesh India Koneru Lakshmaiah Education Foundation Vaddeswaram Jonnalagadda Mohana and Reddem Pravallika Final Year B.Tech CSE Andhra Pradesh India

ISBN: (纸本)9781728185835

To enhance the capability of the learning model in this research paper we have developed a hybrid feature selection method. To defeat the curse of dimensionality, to speed up the classification process, and to get more accurate results a hybrid feature selection model is developed which is a combination of multiple filter methods and a wrapper method. In this model, we employed two sets of Filter Methods-basic filter methods, a correlation filter method and two Statistical Ranking filter methods (ANOVA and ROC-AUC) to generate two different subsets of important features, and a Wrapper method (Recursive Feature Elimination with Cross-Validation) is applied on the combined subset to generate a final subset of important features for better prediction results. Five machine learning algorithms-Logistic Regression (LR), Decision Tree, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour are used to evaluate the classification accuracy. The proposed hybrid method is applied over four low and four microarray datasets. Outputs are compared to find out which algorithm works best with the proposed model as the results diverge with the machine learning algorithm. Precision, Sensitivity, and Specificity are calculated for each outcome and they demonstrate that the suggested method improved the accuracy of the algorithms. © 2021 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted boltzmann Machine 1

Smart Water Management in Agriculture: Enhancing Crop Produc...

引用

1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Gore, K.b. Chidambararaj, N. Kartheesan, L. Ghamande, Manasi Vyankatesh Sarode, Ghanasham C. Kalra, Gourav SNJB's Ls K. B. Jain College of Engineering Department of Mechanical Engineering Nashik India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Technology Department of Engineering Science and Humanities Pune India Dr. D.Y. Patil Institute of Technology Department of Civil Engineering Pune India Department of Mechanical Engineering Ambala India

ISBN: (纸本)9798350352931

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. by implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RbM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DbN and KNN. © 2024 IEEE.

关键词： Irrigation

来源：评论

学校读者我要写书评

暂无评论

IDOL: IoT based intrusion detection using Conv- biLSTM

引用

Australian Journal of Electrical and electronics engineering 2025年

作者： C, Vijayalakshmi V, Padmajothi R, Suguna Devi Whitin, Priscilla S, Rajakumar Rajaretnam, Subraja Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India

Internet of Things (IoT) networks have produced copious amounts of data that can be used to identify intrusions. Network Intrusion Detection Systems are crucial for spotting attacks before they cause any damage as part of a layered defence strategy. In this paper, a novel Intrusion Detection using Optimized deep Learning (IDOL) has been proposed to detect the intrusions like DDoS attacks, botnet attacks, Convergence attacks, and Ransomware attacks. Initially the IXIA PerfectStorm tool display data with malicious attack from the Network Environment and the data are converted in the data Pre-Processing method. Then the needed data are extracted in the feature extraction process by using boW technique. From the Extracted data the Feature selection process will select the essential data using Ant Colony Optimization. Finally, the Conv-biLSTM model will classify the attack into four classes namely DDoS attack, botnet attack, Convergence attack, and Ransomware attack. The proposed IDOL method achieves the highest accuracy rate of 98.93%. Using the UNSW-Nb15 and NSL-KDD datasets, the suggested model achieves an accuracy rate of 98.92% and 98.94% which is greater than the existing techniques such as ML-IMID, IDS-SLoEL, and POS-LightGRM respectively. © 2025 Engineers Australia.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

Enhancing Human-Robot Collaboration through 5G-Enabled Transformation and Image-Guided Control with TYOLOV5 Algorithms 7

Enhancing Human-Robot Collaboration through 5G-Enabled Trans...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Ramesh, P.S. Roy, Priyanka Arun Raj, S.R. Yadav, Vinay Kumar balaram, Allam Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science And Technology Department of Computer Science And Engineering Tamilnadu Chennai India Vellore Institute of Technology Department of Electrical And Electronics Engineering Tamilnadu Vellore India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Factories that prioritize digital transformation experience heightened production rates and gain a competitive advantage by enhancing control and operational efficiency. In this study, image processing techniques were employed to achieve digital transformation objectives, and the data obtained was transmitted via 5G communication to a collaborative robot arm, enabling remote control. The robot arm was fitted with a 3D-printed humanoid hand for bin picking, where each finger was controlled by five servo motors. To manipulate the fingers, a user donned a glove equipped with flex sensors, which conveyed the finger positions to the servo motors, facilitating precise pick and place operations. Additionally, image processing was utilized to accurately control the robot arm's position. The research involved the implementation of tuned YOLO V5 (TYOLOV5) algorithms, using Python software, for object detection within the user's gloves. by comparing this approach with other existing methods, the experiment achieved the highest detection accuracy, demonstrating the effectiveness of the proposed approach in achieving superior recognition and estimation compared to traditional and existing deep learning methods. To validate its efficacy, experiments were conducted in a laboratory setting, where a collaborative robot was deployed for a small part assembly task. The results conclusively highlight the potential of this approach to significantly enhance recognition and estimation capabilities, especially in the realm of human-robot collaboration. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

引用

Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： M. balasubramani b. J. Job Karuna Sagar K. C. Rajheshwari E. Saranya b. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a big brain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited brain COLOR atlas. We also elaborate on his method for processing MR images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

来源：评论

学校读者我要写书评

暂无评论

Jellyfish Search Optimizer with Deep Learning Driven Cervical Cancer Recognition and Classification Model on biomedical Images 2

Jellyfish Search Optimizer with Deep Learning Driven Cervica...

引用

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

作者： Anand, M. Suresh Venkatramulu, S. Vidhya, Sesha S. Chembian, W.T. Yashaswini, b.M. Kumar, Magesh b. Department of Computing Technologies School of Computing SRM Institute of Science & Technology Kattankulathur603203 India Department of CSE Kakatiya Institute of Technology and Science Warangal India Department of ECE RMK College of Engineering and Technology India Department of CSE Vel Tech High Tech Dr Rangarajan Dr Sakuntala Engineering College Avadi Morai India Electronics & Communication Engineering JSS Academy of Technical Education JSSATE-B Campus Dr. Vishnuvardhan Rd Uttarahalli-Kengeri Main Road JSS Campus Rd Srinivaspura Karnataka Bengaluru560060 India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Sathyamangalam India

ISBN: (纸本)9798350383867

The emergence of Cervical Cancer (CC) Recognition and Classification Model for biomedical Images is a main advancement in fields of medical imaging and diagnostics. Leveraging state-of-the-art image processing and machine learning algorithms, this model can accurately detect and classify CC-related abnormalities in medical images, namely colposcopy or Pap smear images. by providing automated and precise assessments, the model empowers medical experts to make timely and informed decisions, which leads to earlier interference and potential life-saving treatment. With its potential to improve diagnostic accuracy and reduce human error, the model holds great promise in the fight against CC, especially in resource-constrained healthcare settings where medical practitioners may be limited. This study presents a Jellyfish Search Optimizer with Deep Learning Driven CC Recognition and Classification (JSODL-CCRC) model on biomedical Images. The JSODL-CCRC technique is mainly developed to investigate the medical images for detection and classification of CC. The JSODL-CCRC approach comprises median filtering (MF) for noise reduction, followed by feature extraction utilizing Inceptionv3 to derive complex image patterns. besides, the JSO based hyperparameter selection process is performed for the Inception v3 model. At last, Extreme Learning Machine (ELM) algorithm is applied to categorize the images which validates efficacy in handling complex biomedical image data. The experimental results stated that the JSODL-CCRC model enables effective detection results over other models. © 2024 IEEE.

关键词： Median filters

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：