检索结果-内蒙古大学图书馆

2nd International Conference on computer, communication and Control, IC4 2024

作者： Deepa, B. Marimuthu, C.N. Geetha, S. Maheswari, S. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%. © 2024 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

CAVIaR crayfish algorithm enabled Deep Kronecker Network for Wormhole attack mitigation on Network Control System 5

CAVIaR crayfish algorithm enabled Deep Kronecker Network for...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Avina, A. Geetha, M.R. Department of Computer science and engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India Department of Electronics and Communication Engineering Ponjesly College of Engineering TamilNadu Nagercoil629003 India

ISBN: (纸本)9798350361155

Wireless Sensor Network (WSN) is expansively employed in the Network Control System (NCS). Wormhole (WH) attack detection is a burdensome process, specifically over WSN. A distributed passive state of WH attack makes the network extremely challenging to identify. Here, the CAVIaR crayfish algorithm enabled Deep Kronecker Network (CCA-DKN) is introduced for mitigation of WH attack on NCS. Firstly, the WSN model is simulated and routing is executed by Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Then, three stages namely in-band (IB)-WH detection, out-of-band (OB)-WH detection, and Neighbour Ratio Threshold (NRT) are accomplished for WH detection. After the accomplishment of the NRT stage, the transmission range stage is performed in OB-WH detection. Thereafter, the Packet Delivery Ratio (PDR) and Round Trip Time (RTT) are done on IB-WH detection. Next, the detection of WH attack is performed by ResNeXt-DSAE. Furthermore, ResNeXt-DSAE is designed by combining ResNeXt and Deep Stacked Autoencoder (DSAE). Lastly, attack mitigation is executed, whereupon a percentage of data reduction is carried out by exploiting the Deep Kronecker Network (DKN) and it is trained by the CAVIaR crayfish algorithm (CCA). Furthermore, CCA is an assimilation of Conditional Autoregressive Value at Risk (CAViaR) and Crayfish Optimization Algorithm (COA). Additionally, CCA-DKN achieved high PDR and throughput of 0.769 and 0.533Mbps as well as low delay of 0.712sec. © 2024 IEEE.

关键词： Wireless sensor networks

来源：评论

学校读者我要写书评

暂无评论

Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

引用

2024 International Conference on communication, computer sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Enhancing Underwater Image Segmentation: A Semantic Approach to Segment Objects in Challenging Aquatic Environment 2

Enhancing Underwater Image Segmentation: A Semantic Approach...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： George, Geomol Anusuya, S. Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai602105 India

Aquatic image segmentation is complex due to the inherent complexity of light attenuation, color shifts, and vision limits in underwater settings. In order to improve underwater image segmentation and the delineation of objects in such conditions, this research study suggests a novel semantic technique. The suggested approach uses cutting-edge deep learning methods and a U-Net architecture with DenseNet-201 at its core. The dense connectivity patterns of DenseNet-201 make it possible to extract useful features, increasing gradient flow and feature reuse across the network. The model uses a combination of Dice Loss and Categorical Focal Loss is the loss function to enhance segmentation performance. It utilizes evaluation metrics like the IoU Score and F-Score, which give detailed insights into the model's performance on underwater picture segmentation tasks. According to the evaluation metrics, the "DenseNet201" architecture is the most effective one for underwater picture segmentation. On the test dataset, it received the highest Mean IoU Score (79.77/%) and F1 Score (84.85)%. Furthermore, it obtained competitive results for each class-wise IoU score, demonstrating strong segmentation performance for different object categories. © 2024 Elsevier B.V.. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Improved Key Agreement Based Kerberos Protocol for M-Health Security

引用

computer Systems science & engineering 2022年第8期42卷 577-587页

作者： P.Thirumoorthy K.S.Bhuvaneshwari C.Kamalanathan P.Sunita E.Prabhu S.Maheswaran Department of Computer Science and Engineeirng Nandha Engineering CollegeErode638052India Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Electrical Electronics and Communication EngineeringGITAM UniversityBengaluru Campus561203India Department of Electronics and Communication Engineering Amrita School of EngineeringCoimbatoreAmrita Vishwa Vidyapeetham641112India Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India

The development of wireless sensor network with Internet of Things(IoT)predicts various applications in the field of healthcare and cloud *** can give promising results on mobile health care(M-health)and Telecare medicine information systems.M-health system on cloud Internet of Things(IoT)through wireless sensor network(WSN)becomes the rising research for the need of modern *** devices attached to the patients’body which is connected to the mobile device can ease the medical *** is the key connect for optimal performance of the m-health system that share the data of patients in wireless networks in order to maintain the anonymity of the *** paper proposed a secure transmission of M-health data in wireless networks using proposed key agreement based Kerberos *** patients processed data are stored in cloud server and accessed by doctors and *** data transfer between the patients,server and the doctors are accessed with proposed protocol in order to maintain the confidentiality and integrity of *** efficiency of the proposed algorithm is compared with the existing *** computing 100 devices it consumes only 91milllisecond for computation.

关键词： Health monitoring authentication preparation protocol kerberos key agreement

来源：评论

学校读者我要写书评

暂无评论

Machine Learning Approaches for COVID-19 Sentiment Analysis: Unveiling the Power of BERT 14

Machine Learning Approaches for COVID-19 Sentiment Analysis:...

引用

2024 IEEE 14th Annual Computing and communication Workshop and Conference, CCWC 2024

作者： Srivastava, Shobhit Sarkar, Mrinal Kanti Chakraborty, Chinmay University of Engineering and Management Jaipur India University of Engineering and Management Department of Computer Science & Engineering Jaipur India Bit Department of Electronics and Communication Engineering Jharkhand Mesra India

ISBN: (纸本)9798350360134

The exponential growth of social media technology has fueled an unprecedented surge in the free flow of information. The outbreak of the Coronavirus disease (COVID-19) has left an indelible mark on society, profoundly impacting its social and psychological fabric. Amidst this global health crisis, social media platforms have emerged as vibrant arenas for discussing the COVID-19 pandemic. Delving into the emotional undercurrents of social media discourse is paramount to safeguarding societies from the pandemic's devastating effects. Sentiment analysis, a powerful tool for text analysis, proves particularly challenging when applied to the nuanced and often colloquial language of tweets. This methodology automates the identification and assessment of opinions and emotions encapsulated within unstructured data. Sentiment analysis tools meticulously examine texts sourced from a variety of outlets, which includes data sourced from spam mail, fake scrutiny of public opinion, malicious retrospection, media posts, and articles on the web, extracting meaning from these diverse repositories of human expression. Using the applications of various language learning techniques and ML techniques, behavioral analysis assigns values to the sentiment following the entities, topics, themes, and categories, facilitating a comprehensive assessment of sentiments. Machine learning algorithms, having been trained on extensive collections of text-emotion examples, continuously enhance their ability to discern sentiment without human intervention. Particularly crucial during a pandemic, the analysis of social media texts unveils valuable insights into prevailing sentimental trends, contributing to a profound understanding of societal needs and the anticipation of future trends. In this article, we use a powerful language-learning tool with some hybrid features. We use BERT with multitailed CNN (Deep CNN) which includes three layers of the CNN model. An examination of various research studies

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Vechicular Accident Detection and Alert Generation Using IoT 5

Vechicular Accident Detection and Alert Generation Using IoT

引用

5th International Conference on Intelligent communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Geetha, M. Janakan, D. Nandhakumar, E. Sanjay Kumar, S. Kaviyarasen, B.C. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Karur India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Karur India

ISBN: (纸本)9798350385649

Smart accident detection systems are those that recognize accidents automatically. The Internet of Things, or IoT, can connect the detectors and transmit the information to the destination. It can also perform operations based on data collected by detectors, which can aid in the detection of accidents. The information obtained from detectors is also provided to the appropriate authorities, like police and emergency medical services. This research study examines some of the most modern methods of detecting car accidents using IoT technologies. The challenges faced by existing methodologies and a porotype model has also been developed to detect the accidents. © 2024 IEEE.

关键词： Tracking (position)

来源：评论

学校读者我要写书评

暂无评论

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly Detection 3

Next-Gen Manhole Monitoring: Autoencoder-Assisted Anomaly De...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Krishnan, R. Santhana Gopikumar, S. Muthu, A. Essaki Raj, J. Relin Francis Kumari, D. Abitha Malar, P. Stella Rose Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Civil Engineering Chennai India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical AndTechnical Sciences Department of Electronics and Communication Engineering Chennai India Rmk Engineering College Department of Computer Science and Engineering Thiruvallur India J.P. College of Engineering Department of Electronics and Communication Engineering Thenkasi India

ISBN: (纸本)9798350375190

In various regions, the maintenance of manholes is imperative, given the potential consequences for public health and safety. Neglecting this responsibility can result in severe outcomes, including loss of lives and the spread of diseases within the community. To address these challenges, an advanced anomaly detection system is proposed, leveraging deep learning with Autoencoders. Notably, the entire system operates on solar power, aligning with a commitment to environmentally sustainable practices. At the core of the system's functionality is the Autoencoder, a deep learning model to differentiate complex manhole conditions. The Autoencoder is trained on normative sensor data, capturing nuanced patterns inherent in routine manhole states. This training enables the Autoencoder to perform real-time anomaly detection by swiftly identifying deviations such as the presence of noxious gases, irregular sewage levels, or unauthorized attempts at access. When anomalies are detected, signaling potential hazards, the Autoencoder triggers alert messages disseminated to municipal authorities via a cloud server. This proactive approach empowers timely intervention, particularly critical in scenarios involving toxic gases, ensuring the safeguarding of maintenance personnel. Additionally, the system integrates an NFC reader, streamlining authorized access for designated personnel while upholding robust security measures. In essence, the incorporation of Autoencoders into the system furnishes an intelligent anomaly detection infrastructure for manhole surveillance. By continually adapting to normative patterns, the Autoencoder enhances the system's capacity to identify and promptly communicate potential risks, thereby improving the overall safety and efficiency of manhole maintenance practices. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease 2

A Comprehensive Analysis of Identifying Patients with Chroni...

引用

2nd International Conference on computer, communication and Control, IC4 2024

作者： Geetha, S. Marimuthu, C.N. Maheswari, S. Deepa, B. Anna University Faculty of Information and Communication Engineering Chennai600025 India Nandha Engineering College Department of Electronics and Communication Engineering Erode638052 India Nandha Engineering College Department of Computer Science and Engineering Erode638052 India

ISBN: (纸本)9798350387933

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients' health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies. © 2024 IEEE.

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

An Active Reconfigurable Mixer in GaN Technology for 5G Power Amplifier Wi-Fi communications 6

An Active Reconfigurable Mixer in GaN Technology for 5G Powe...

引用

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

作者： Kumar, Rajesh Babu, Tosif Abdul Gaffar Kanaujia, Binod Kumar Tiwari, Devesh Department of Computer Science & Engineering Galgotias College of Engineering &Technology Greater Noida India Department of Electronics & Communication of Engineering PIET Gujarat Vadodara India Department of Electronics & Communication of Engineering National Institute of Technology Jalandhar Jawaharlal Nehru University New Delhi110067 India Department of Computer Science & Engineering Graphic Science and Engineering Graphic Era Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

The mixer's key attributes in this article are its high power output, high conversion gain, high efficiency, and good isolation between board positions. We have designed and implemented a mixer that can be reconfigured dynamically. Using a voltage gain of 12.3 dB, the local oscillator (LO) signal is amplified. The purpose of the active reconfigurable mixer is to enhance the cross-driver platform's performance. Alongside, with the occurrence of a mixer a high-power level is attained. It is noteworthy to highlight that the third-order intermodulation (IIP3) of the new configuration is -9 dBm, which is regarded as negligible. The mixer runs at 6 GHz and is powered by 0.15-µm GaN technology. © 2024 IEEE.

关键词： Gallium nitride

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：