检索结果-内蒙古大学图书馆

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Virushabadoss, S. Anithaashri, T.P. Nivedha, M. Krishnan, S. Hari Karuppannan, Anand Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Dept. of Computer Science and Engineering Chennai India Arasu Engineering College Department of Electronics and Communication and Engineering Kumbakonam India Sanskrithi School of Engineering Department of Electronics and Communication and Engineering Puttaparthi India Gnanamani College of Technology Department of Electronics and Communication and Engineering Namakkal India

ISBN: (纸本)9798350375442

This paper suggests a complete security architecture that blends quantum key distribution (QKD) with cutting-edge machine learning methods to improve the Software-Defined Networking (SDN) control plane. By creating secure channels of communication through QKD, the framework ensures integrity and secrecy of control messages between the SDN controller and network devices. Furthermore, network traffic is continuously monitored and analyzed using machine learning techniques, enabling the detection of unusual behaviors that might be warning signs of upcoming security problems. Tests utilizing Mininet as the network interface and Open Daylight as the SDN controller proved the efficacy of the suggested method. The outcomes revealed a minimal computational overhead, exceptional detection accuracy up to 98.1%, and low false positive rates, substantiating the system's ability to effectively manage varied network conditions and traffic volumes. These results underscore the potential of integrating QKD with robust machine learning strategies to secure the SDN control plane against sophisticated threats. © 2024 IEEE.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

Enhancing Stockpile Management Through Deep Learning with a Focus on Demand Forecasting and Inventory Optimization

Enhancing Stockpile Management Through Deep Learning with a ...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Sheela, S. Latha, A.P. Jyothi, S. Vidyarani, H.J. Komala, M. Sharmila, N. Mallikarjunaswamy, S. Pavithra, G.S. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Rns Insitute of Technology Dept of Computer Science and Alml Bengaluru India

ISBN: (纸本)9798350388602

Stockpile management is essential for industries to efficiently meet customer demands while minimizing costs. Traditional methods like the Economic Order Quantity (EOQ) model and ARIMA often struggle with adaptability and accuracy, leading to inefficiencies such as overstocking or stockouts. This study proposes using Long Short-Term Memory (LSTM) networks to improve demand forecasting in stockpile management. LSTMs are designed to handle sequential data, capturing complex patterns and long-term dependencies that conventional methods miss. The LSTM-based approach achieved up to a 0.30% improvement in forecasting accuracy, resulting in more precise inventory control and better alignment with actual demand. The proposed system, termed Deep Inventory Management (DIM), incorporates LSTM models that adapt to changes in demand by considering factors such as seasonality and trends. This adaptive approach allows for proactive inventory predictions, enhancing decision-making in procurement and stock levels. Key abbreviations include LSTM (Long Short-Term Memory), EOQ (Economic Order Quantity), and DIM (Deep Inventory Management). By aligning inventory more closely with demand, this deep learning approach enhances operational efficiency, reduces costs, and improves customer satisfaction, making it a valuable advancement over traditional inventory management methods. © 2024 IEEE.

关键词： Semantic Segmentation

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence 8

Deep Learning based Recognition of Auto Covariance Signature...

引用

8th International Conference on electronics, Communication and Aerospace technology, ICECA 2024

作者： Balamurugan, K.S. Geetha Rani, K. Sadish Sendil, M. Kumutha, D. Surendran, R. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India Department of Computer Science and Engineering Bangalore560069 India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Vel Nagar Avadi Chennai India Jeppiaar Institute of Technology Sriperumudur Department of Electronics and Communication Engg Tamilnadu India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai602105 India

ISBN: (纸本)9798350367904

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively. © 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

Chatbot and its Impact on the Retail Industry 3

Chatbot and its Impact on the Retail Industry

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. Thota, Ragupathi Keerthanadevi, R. Department of Computer Information Systems University of the Central Missouri Warrensburg United States Department of AI and ML Bangalore Institute of Technology Bengaluru India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India Department of Information Technology Jawaharlal Technological University Hyderbad India Department of Computer Science and Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (纸本)9798331527549

This study explores the transformative potential of chatbots in the retail industry, highlighting their role in enhancing customer engagement, optimizing business processes, and revolutionizing the nature of retail interactions. Through a comprehensive review of existing literature and case studies, the paper delves into the cognitive processes underlying human-chatbot interactions and examines their impact on customer experience, satisfaction, and loyalty. Chatbots serve a wide array of operational functions, including inventory management, automated order processing, data-driven analytics, and employee training. These functionalities streamline workflows, improve efficiency, and maximize resource utilization. The paper further explores the innovative applications of chatbots, including AI-driven personalization, conversational commerce, and seamless integration with emerging technologies. By analyzing both the current applications and challenges of chatbot technology in retail, the study uncovers the exciting opportunities they present for shaping the future of retail by fostering more personalized, efficient, and interactive customer experiences. © 2025 IEEE.

关键词： Inventory control

来源：评论

学校读者我要写书评

暂无评论

A Highly Efficient Ternary Adder and Carbon Nanotube FET in VLSI- A Review

A Highly Efficient Ternary Adder and Carbon Nanotube FET in ...

引用

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Kumar, Sandeep Singh, Arjun Singh, Arun Kumar Department Of Electronics and Communication Engineering G.L.BAJAJ Institute of Technology & Management Greater Noida India Department Of Computer Science & Engineering Greater Noida Institute of Technology Greater Noida India

ISBN: (纸本)9798350355338

This review article shows the benefits of replacing binary logic with ternary logic and the application of carbon nanotube FET in adder circuits that reduce interconnect and improve the delay, power dissipation, and speed. Future nanotechnology faces the limitation of CMOS like DIBL, the effect of the short channels, high leakage current, and temperature-dependent threshold voltage. These are overcome by carbon nanotube FET. CMOS was replaced by carbon nanotube FET due to having similarity in structure as the channel of its form by tubes of carbon which works on ballistic transport and chirality. Currently, numerous researches are going on to develop and investigate the use of Carbon Nanotube FET in half and full adder. In this article, a comparison study for half and full adder using carbon nanotube FET is done and parameters like propagation delay, power dissipation, and transistor count are compared. This analysis shows that Carbon nanotube FET- based digital circuits are much more efficient as compared to CMOS logic. The performance parameter and results are simulated using HSPICE software. © 2024 IEEE.

关键词： CMOS integrated circuits

来源：评论

学校读者我要写书评

暂无评论

Principles and Potentials of F5G: The Fifth Generation Fixed Network 1

Principles and Potentials of F5G: The Fifth Generation Fixed...

引用

1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Routray, Sudhir K. Javali, Abhishek Sharmila, K.P. Pappa, M. Jha, Mahesh K. Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

In general, 5G is understood to be the fifth generation of mobile communications. However, there is also a fifth generation of wired communications which is led by the optical fiber technologies. This technology too has several attractive features like the wireless mobile 5G. In this article, we present the basic concepts of Fifth Generation Fixed Network (F5G). We show its characteristics and explain how it is going to change the communication landscape. We present the key benefits of F5G and show the main motivations behind its development. There are several applications of F5G and many emerging applications are also being introduced every year. Therefore, this newly developed technology has a lot of commercial importance in the modern world. In addition to the above, F5G is going to be one of the main rivals of 5G in the fixed environment. It is expected that F5G will outperform all its contemporary access area technologies in the fixed domain in the next few years. In this article, we briefly present its applications and future economic prospects. © 2023 IEEE.

关键词： Optical fibers

来源：评论

学校读者我要写书评

暂无评论

Trajectory-Driven Efficiency: An Energy-Efficient Opportunistic Routing Protocol for Industrial IoT Wireless Sensor Networks 10

Trajectory-Driven Efficiency: An Energy-Efficient Opportunis...

引用

10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajulu, G. Govinda Murugesan, M. Mahaboog Basha, S.K. Kalaiselvan, S.A. Kumar, M. Lenin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India

ISBN: (纸本)9798350353068

The rapid expansion of Wireless Sensor Networks (WSNs) in the realm of Industrial Internet of Things (IIoT) demands sophisticated routing protocols to ensure energy efficiency and reliable communication. This paper introduces a novel Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction (EORB-TP) to address these challenges in industrial environments. The research identifies the need for dynamic adaptation to industrial dynamics and changing environmental conditions, prompting the development of trajectory prediction models using Hidden Markov Models (HMMs). EORB-TP intelligently leverages these models for dynamic routing decisions, strategically optimizing data transmission paths to minimize energy consumption. The proposed system's efficiency is demonstrated through a comprehensive experimental study, showcasing superior performance in terms of network lifetime, energy consumption, end-to-end delay, and routing cost compared to existing protocols. This innovative protocol offers a promising solution for enhancing the sustainability and efficiency of Wireless Sensor Networks in Industrial IoT applications, paving the way for resilient and resource-conscious industrial monitoring systems. © 2024 IEEE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

ML Based Intrusion Detection Scheme for various types of attacks in a WSN using C4.5 and CART classifiers

引用

Materials Today: Proceedings 2023年 80卷 3769-3776页

作者： Gite, Pratik Chouhan, Kuldeep Murali Krishna, K. Kumar Nayak, Chinmaya Soni, Mukesh Shrivastava, Amit Department of Computer Science and Engineering IPSA-IES Indore AB Road MP Indore India Department of Computer Science and Engineering I.T.S Engineering College Greater Noida India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Faculty of Emerging Technology Sri Sri University Odisha751001 India Department of Computer Science & Engineering Jagran Lakecity University Bhopal India Department of Computer Science Engineering Swami Vivekanand College of Engineering Khandwa Road Near Tolnaka Vivekanand Knowledge City Indore India

A key component for preserving a WSN's integrity is designing an intrusion detection system (IDS). This article covers several sorts of security threats that may occur in a WSN. It proposes a detection of malicious nodes in a WSN. This system identifies four common assaults (i.e., black hole, wormhole, gray hole, and DDoS attacks) and is based on a Base Station machine learning (ML) algorithm (BS). The suggested ML algorithm continually analyzes data patterns from each node. Based on this technique, BS recognizes the network's harmful behavior and provides notifications to neighbor nodes to prevent attacker. First, the various assaults are examined and their characteristics are derived in terms of network parameters. Furthermore, the gathered pattern trains an ML algorithm. Then, the attacker node is effectively and properly classified within the BS. The NS2 simulator simulated the needed secure WSN. The experimental findings revealed excellent accuracy of the suggested method for attack detection. This precision can enhance network performance in terms of energy usage and packet delivery (PDR). © 2021

关键词： Base stations

来源：评论

学校读者我要写书评

暂无评论

IoT-Deep Learning Based Activity Recommendation System

引用

computer Systems Science & engineering 2023年第5期45卷 2001-2016页

作者： Sharmilee Kannan R.U.Anitha M.Divayapushpalakshmi K.S.Kalaivani Department of Electronics and Communication Engineering Nandha Engineering CollegePerundurai638052TamilnaduIndia Department of Computer Science Sona College of Arts and ScienceSalem636005TamilnaduIndia Department of Artificial Intelligence and Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and TechnologyHyderabad500090TelanganaIndia Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060TamilnaduIndia

The rising use of mobile technology and smart gadgets in the field of health has had a significant impact on the global *** professionals are increasingly making use of the benefits of these technologies,resulting in a major improvement in health care both in and out of clinical *** Internet of Things(IoT)is a new internet revolution that is a rising research area,particularly in health *** Monitoring Systems(HMS)have progressed rapidly as the usage of Wearable Sensors(WS)and smartphones have *** existing framework of conventional telemedicine’s store-and-forward method has some issues,including the need for a nearby health centre with dedicated employees and medical devices to prepare patient ***’health can be continuously monitored using advanced WS that can be fitted or embedded in their *** research proposes an innovative and smart HMS,which is built using recent technologies such as the IoT and Machine Learning(ML).In this study,we present an innovative and intelligent HMS based on cutting-edge technologies such as the IoT and Deep Learning(DL)+Restricted Boltzmann Machine(RBM).This DL+RBM model is clever enough to detect and process a patient’s data using a medical Decision Support System(DSS)to determine whether the patient is suffering from a major health problem and treat it *** recommended system’s behavior is increasingly investigated using a cross-validation test that determines various demographically relevant standard *** a healthcare DSS,this framework is clever enough to detect and analyze a patient’s *** results further reveal that the proposed system is efficient and clever enough to deliver health *** data reported in this study demonstrate the *** device is a low-cost solution for people living in distant places;anyone can use it to determine if they have a major health problem and seek treatment by contacting nearby hospitals.

关键词： Deep learning IoT healthcare system activity recommender system body sensors

来源：评论

学校读者我要写书评

暂无评论

Sexual Harassment Detection using Machine Learning and Deep Learning Techniques for Bangla Text 3

Sexual Harassment Detection using Machine Learning and Deep ...

引用

3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Islam, Mujahidul Rahman, Maqsudur Ahmed, Md Tofael Muhammad Islam, Abu Zafor Das, Dipankar Hoque, Mohammed Moshiul Comilla University Department of Information and Communication Technology Bangladesh University of Rajshahi Department of Electrical & Electronics Engineering Bangladesh Chittagonj University of Engineering and Technology Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350345360

Harassment is a kind of act that annoys or upsets someone. Harassment can be classified into different categories. Sexual harassment is one of them. Sexual harassment is a type of harassment that involves the use of implicit or explicit sexual overtones, including the inappropriate and unwelcome promises of rewards in exchange for sexual favors. At present time, the technology has become more advance and spread all over the place. That gave the toxic people a huge opportunity to spread toxicity in online platforms. Because of the increasing amount Bangla text in different social media platforms, we also need to filter such kinds of offensive Bangla texts. The objective of this research is to detect sexual harassment from Bangla text and classify them by using machine learning and deep learning algorithms as well as prevents them. In the experiment, we combined TF-IDF with different machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, AdaBoost, SGD, Logistic Regression, KNN, SVM and got accuracy of 74.9%, 75.6%, 70.0%, 70.1%, 75.2%, 75.7%, 65.2%, 76.5% respectively. Deep learning algorithms like CNN, LSTM, hybrid CNN-LSTM were also used and achieved accuracy of 89% for all of them which is comparatively better than machine learning techniques. © 2023 IEEE.

关键词： Sentiment analysis

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：