检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Adnan, Myasar Mundher Maheshkumar, N. Praveena, J. Patidar, deepak Aarju Ambika The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Karpagam Academy of Higher Education Faculty of Engineering Department of Science and Humanities Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dapartment of Cse Chennai India

ISBN: (纸本)9798350390759

The aim of this research therefore is to propose and design bio-inspired routing protocols in UWSNs using PSO. High latency and low bandwidth, coupled with high error rates, make underwater routing a different ball game than routing in normal situations and traditional mediums. The proposed PSO-based protocols also consider factors like power utilization, coverage distance, and priority of data packets to make better routing choices thus helping to improve the efficiency and responsiveness of the UWSNs. The compare analysis of the proposed protocols with the earlier protocols, which are also developed based on AOdV and dSR, proves high performance of the PSO based protocols in the energy consumption, data delivery ratio, end to end delay and the network lifetime within all the realistic underwater environments analyzed by the simulations. Such advancement would help to improve the communication system in UWSNs, presenting new approaches for efficient communication in the underwater environment. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

GAMe-BilSTM: a novel modified metaheuristic deep learning technique for non-ST-segment elevation myocardial infarction classification

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 1063-1078页

作者： Patil, Sachin Sambhaji Mohite-Patil, Tanajirao Bapuso Kalipindi, Murali Sarvani, Chalamalasetty Ramesh Babu, P. Maheshwari, Shikha Department of Electronics and Communication Engineering School of Engineering and Sciences MIT-ADT University Loni Kalbhor Maharashtra Pune India Department of Electronics and Telecommunication Engineering D. Y. Patil College of Engineering and Technology Maharashtra Kasaba Bawada Kolhapur India Department of Artificial Intelligence and Machine Learning Vijaya Institute of Technology for Women (Affiliated to JNTUK) Andhra Pradesh Enikepadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science College of Engineering and Technology Wollega University Oromia Region Nekemte Ethiopia Centre for Distance & amp Online Education Manipal University Rajasthan Jaipur India

Outdated approaches encounter challenges in discerning subtle electrocardiographic changes in real-time associated with Non-ST-Segment Elevation Myocardial Infarction (NSTEMI), leading to suboptimal outcomes, characterized by low accuracy, prolonged processing times, and slow convergence speeds. To address these issues, the introduced framework integrates a Graph Attention Meerkat based-Bidirectional long Short Term Memory (GAMe-BilSTM) to overcome interpretability limitations in traditional approaches, providing insights into critical clinical variables for more informed NSTEMI classification. GAMe-BilSTM adapts to missing values, further enhancing model robustness. The effectiveness of the newly introduced NSTEMI classification approach is substantiated through an ablation experiment, where variations in loss functions, learning rates, and optimizers are systematically explored. This experimentation confirmed the superiority of the proposed classification method. This research underscores the integral role played by real-time electrocardiographic signal image data in enhancing the accuracy (99.99%) and efficacy of the introduced methodology, thereby contributing significantly to the advancement of NSTEMI classification techniques in the realm of cardiovascular research and diagnosis. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cardiovascular diagnostics deep learning Electro cardio gram GAMe-BilSTM Myocardial infarction

来源：评论

学校读者我要写书评

暂无评论

Automated analysis of fundus images for the diagnosis of retinal diseases: a review

引用

Research on Biomedical engineering 2024年第1期40卷 225-251页

作者： Berto, Alexa Scarpa, Fabio Tsiknakis, Nikos Manikis, Georgios Fotiadis, dimitrios I. Marias, Kostas Scarpa, Alberto D-Eye s.r.l. Padua Italy Department of Information Engineering University of Padova Padua35131 Italy Heraklion70013 Greece Department of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina45115 Greece Department of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Ioannina45110 Greece Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion71004 Greece

Purpose: Colour fundus images are widely used in diagnosis treatment decision of several retinal diseases such as diabetic retinopathy (dR), glaucoma and age-related macular degeneration (AMd). These very common conditions must be detected early and monitored to prevent progression and avoid permanent damage. Fundus images revealed to be useful also for the diagnosis of cataracts. In recent years, various automatic diagnostic support methods have been proposed in the literature, with the aim of facilitating widespread screening and obtaining quantitative, objective, and reproducible information. Methods: In this review paper, an overview of traditional, machine learning and modern deep learning techniques for ophthalmic disease diagnosis (i.e. glaucoma, dR, AMd, and cataract) using retinal fundus images is presented. In addition, various publicly available image datasets used for such purposes are described. Results: The current main challenges and findings are identified, as well as common aspects and discrepancies between the various methods developed for the various diseases. Conclusion: The overview of what has been done about all pathologies, rather than only on a specific one, could favour a migration of the best solutions and, hopefully, the development of a more precise and clinically useful automatic analysis of all pathologies. Important critical insights and research trends are also discussed. Graphical abstract: (Figure presented.) © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2023.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

Efficient text document clustering with new similarity measures

引用

International Journal of Business Intelligence and data Mining 2021年第1期18卷 109-126页

作者： lakshmi, R. Baskar, S. Department of Computer Science and Engineering K.L.N. College of Engineering Sivagangai District Tamilnadu India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai Tamilnadu India

In this paper, two new similarity measures, namely distance of term frequency-based similarity measure (dTFSM) and presence of common terms-based similarity measure (PCTSM), are proposed to compute the similarity between two documents for improving the effectiveness of text document clustering. The effectiveness of the proposed similarity measures is evaluated on reuters-21578 and WebKB datasets for clustering the documents using K-means and K-means++ clustering algorithms. The results obtained by using the proposed dTFSM and PCTSM are significantly better than other measures for document clustering in terms of accuracy, entropy, recall and F-measure. It is evident that the proposed similarity measures not only improve the effectiveness of the text document clustering, but also reduce the complexity of similarity measures based on the number of required operations during text document clustering. Copyright © 2021 Inderscience Enterprises ltd.

关键词： Entropy

来源：评论

学校读者我要写书评

暂无评论

Internet of Things Assisted Automated Ransomware Recognition using Harmony Search Algorithm with deep learning

Internet of Things Assisted Automated Ransomware Recognition...

引用

2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Karunakaran, S. Manimaraboopathy, M. Maharajothi, M. Kirubasagar, P. Subburaj, T. Varun, S. Kongu Engineering College Department of Computer Technology Erode Perundurai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Electronics and Communication Engineering India RajaRajeswari College of Engineering Department of Mca Bangalore India

ISBN: (纸本)9798350313987

The Internet of Things (IoT) denotes a networking system of interrelated equipment's that is capable of gathering and interchanging information in the Internet system with no human interference. A few such devices are sensors, wearables, smart appliances, industrial machines, cameras, and much more. Under the cyber safety context, IoT gadgets can be employed for gathering useful information and giving added data for recognizing possible ransomware challenges. Ransomware is a kind of malevolent software which will be encrypting the data of a victim, making it unavailable until the attacker is paid off. Automatic detection of ransomware encompasses the utilization of Artificial Intelligence (AI), Machine learning (Ml), or other automatic techniques for classifying and detecting ransomware outbreaks depending on behaviours, particular patterns, or characteristics. This manuscript presents an IoT-assisted Automated Ransomware Recognition using a Harmony Search Algorithm with a deep learning (ARR-HSAdl) approach. The ARR-HSAdl technique presents three major phases of operations and intends a proper ransomware classification. At the initial stage, the ARR-HSAdl technique uses HSA to elect a set of features. Next, the ARR-HSAdl technique utilizes the long Short-Term Memory (lSTM) approach for the ransomware detection process. Finally, the ARR-HSAdl technique uses a Root Mean Square Propagation (RMS Prop) optimizer for the procedure of tuning. The investigational output of the ARR-HSAdl methodology is experimented on the ransomware dataset. The obtained values inferred that the ARR-HSAdl methodology exhibits its betterment with compared models. © 2023 IEEE.

关键词： Artificial Intelligence Cybersecurity Feature selection Internet of Things Ransomware

来源：评论

学校读者我要写书评

暂无评论

Topic Controlled Steganography via Graph-to-Text Generation

引用

computer Modeling in engineering & Sciences 2023年第7期136卷 157-176页

作者： Bowen Sun Yamin li Jun Zhang Honghong Xu Xiaoqiang Ma Ping Xia School of Computer Science and Information Engineering Hubei UniversityWuhan430062China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Yichang Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang443002China Department of CSIS Douglas CollegeNewWestminsterBCV3L 5B2Canada College of Computer and Information Technology China Three Gorges UniversityYichang443002China

Generation-based linguistic steganography is a popular research area of information *** text generative steganographic method based on conditional probability coding is the direction that researchers have recently paid attention ***,in the course of our experiment,we found that the secret information hiding in the text tends to destroy the statistical distribution characteristics of the original text,which indicates that this method has the problem of the obvious reduction of text quality when the embedding rate increases,and that the topic of generated texts is uncontrollable,so there is still room for improvement in *** this paper,we propose a topic-controlled steganography method which is guided by graph-to-text *** proposed model can automatically generate steganographic texts carrying secret messages from knowledge graphs,and the topic of the generated texts is *** also provide a graph path coding method with corresponding detailed algorithms for graph-to-text *** from traditional linguistic steganography methods,we encode the secret information during graph path coding rather than using conditional *** test our method in different aspects and compare it with other text generative steganographic *** experimental results show that the model proposed in this paper can effectively improve the quality of the generated text and significantly improve the concealment of steganographic text.

关键词： Information hiding linguistic steganography knowledge graph topic controlled text generation

来源：评论

学校读者我要写书评

暂无评论

Earthquake Early Warning System Utilizing an CNN-lSTM-Tl Based Method for detection and Parameters Classification 1

Earthquake Early Warning System Utilizing an CNN-LSTM-TL Bas...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Kalpanadevi, d. Siva, M. Chavan, Chetan Shashikant Kaliappan, S. Jothilakshmi, S. Venkata Ramana, K. School of Science Gitam University Department of Computer Science Karnataka Bengaluru India Easwari Engineering College Department of Civil Engineering Chennai India Dr. D. Y. Patil Institute of Technology Department of Civil Engineering Pimpri Pune India Lovely Professional University Department of Division of Research and Development Punjab Phagwara India RMK College of Engineering and Technology Department of Chemistry Kavaraipettai India QIS College of Engineering and Technology Department of CSE Ongole India

ISBN: (纸本)9798350371802

The obvious first line of protection against powerful earthquake motion is to reinforce houses and other structures. The goal of real-time earthquake catastrophe prevention, in contrast to real-time seismology, is to mitigate damage while an earthquake is still underway. A disaster preventive measure that can be put into action in real-time in the event of an earthquake requires an early warning system (EEW). In order to avoid disasters, should not rely solely on EEW. The order of preprocessing, feature selection, and training the model must be meticulously followed. The preparation phase includes data encoding and normalization. Feature selection incorporates principal component analysis and linear discriminant analysis. It utilized CNN-lSTM-Tl for the model's training. The results demonstrate a remarkable 96.49% accuracy. © 2024 IEEE.

关键词： Earthquakes

来源：评论

学校读者我要写书评

暂无评论

Modified Artificial Hummingbird Algorithm with deep learning for Pest detection through leveraging Pattern Recognition-based Fusion Approach 7

Modified Artificial Hummingbird Algorithm with Deep Learning...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Rubadevi, B. Suresh, Helina Rajini Abdullah, Ibrahim Choudhary, Prashant Kumar Choudhary, Minu Mathurkar, Piyush Arunai Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvannamalai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Al-Turath University Computer Science Department Baghdad Iraq Lovely Professional University Department of Electronics and Communication Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9798350372816

The limitations of manual inspection and simple algorithms highlight the need for advanced insect detection systems in agriculture. The study presents the Modified Artificial Hummingbird Algorithm with deep learning (MAHAdl), a significant breakthrough in pest detection. MAHAdl combines the speed and efficacy of the Artificial Hummingbird Algorithm (AHA) with deep learning models such as Recurrent and Convolutional Neural Networks. Combining pattern recognition with the fusion approach improves pest detection accuracy while reducing false positives and negatives. Accurate pest identification is essential for effective agricultural management, which MAHAdl provides through data collection, preprocessing, and algorithmic integration. MAHAdl outperforms existing systems in terms of accuracy (0.94), recall (0.95), precision (0.92), computational economy (12 hours of training time, 0.5 seconds/image inference time), and decrease in false positives and negatives. The innovative technique provides enhanced agricultural output and sustainability, representing a significant leap in intelligent pest management. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Hybrid CNN-lSTM Machine learning Algorithm for driver distraction detection 2

Hybrid CNN-LSTM Machine Learning Algorithm for Driver Distra...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kothuri, Sri Raman Sujay, V. Sreedhar, Siva Satya Juyal, Prachi Jyothi Prasad, M. Bhuvaneshwari, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of CSE Tamil Nadu Chennai India Krishna University College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Machilipatnam India Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh India Uttarakhand Dehradun India RMD Engineering College Department of ECE Tamil Nadu Chennai India Paavai Engineering College Department of ECE Tamil Nadu Chennai India

ISBN: (纸本)9798350317558

driver distraction is a critical safety concern on roadways, with potentially life-threatening consequences. The proposed system combines the spatial characteristic extraction capabilities of CNNs with the temporal modeling capabilities of lSTMs to effectively capture and analyze distractions in real-time. In the hybrid model, the CNN component processes visual input data, such as images or video frames, to extract spatial features that characterize driver behavior. These features are then fed into the lSTM component, which accounts for the temporal context and dependencies within the data. This approach promises to significantly enhance road safety by providing an intelligent system that can alert drivers and authorities to potential distractions, thereby reducing the risk of accidents. This study signifies a pivotal advancement in the progression of advanced driver assistance systems (AdAS) and driverless vehicles. The hybrid CNN-lSTM technique offers a promising route for safer and more reliable transportation systems with 93 % accuracy in detecting drivers' distractions. © 2024 IEEE.

关键词： activity recognition Advanced driver Assistance Systems distraction detection long Short-Term Memory network object detection spatial features temporal

来源：评论

学校读者我要写书评

暂无评论

An Efficient SFlA and CUCKOO Search Hybridization for Source distribution in Cloud Computing

An Efficient SFLA and CUCKOO Search Hybridization for Source...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Malsoru, Vankudothu Patil, Amareshwari Kusuma, S.M. Shivakanth, Gandla Samanvita, N. Cmr Technical Campus Department of It Hyderabad India Pda College of Engineering Department of Computer Science & Engineering Kalaburagi India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India

ISBN: (纸本)9798350398465

Cloud calculating is a computing prototype that offers inexpensive, scalable computing resources like CPU, storage, and network bandwidth. It provides a pay-as-you-go model for gaining instantaneous online access to a shared pool of resources. This strategy is proposed to cut down on the required amount of computing time. In the most recent paper, authors employ a hybrid approach to resource allocation by combining two powerful optimization algorithms: the Shuffled Frog leaping Algorithm (SFlA) and the Cuckoo Search (CS) Algorithm. With this algorithm, we can get around the computational time constraints of the HABCCS algorithm and the krill herd algorithm. It's easily scalable, so even niche uses can benefit. The SFlA has the potential to generate a global optimization solution and comes with the added benefits of faster convergence and simpler implementation. Algorithms from the field of computer science are used in difficult situations because of their advantageous simplicity in evaluation. The knapsack problems that normally crop up during the phase of allocating resources are also addressed. The efficacy of the third strategy is evaluated. The investigational consequences demonstrate the greater presentation of the projected method equated to the state-of-the-art methods. © 2023 IEEE.

关键词： Cloud computing

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：