检索结果-内蒙古大学图书馆

Renewable Energy Frontiers Exploring Sustainable Energy Sources And Preservation Techniques for Ecosystem Maintenance and Biodiversity Conservation 4

Renewable Energy Frontiers Exploring Sustainable Energy Sour...

引用

4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhabliya, Dharmesh Singh, K. Ranjith Minhas, Deepak Nishanthi Gupta, Shikhar Lakshman, K. Vishwakarma Institute of Technology Department of Information Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Cse Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - UG Management Karnataka Bangalore India

ISBN: (纸本)9798331518981

We introduce Sustainable Energy Optimization via Eco Synthesis (SEOTES), a novel paradigm that promises to revolutionize green energy planning. EcoOptimize, BioDiversityGuard, and Community Empower are SEOTES's complicated initiatives that reduce waste, increase efficiency, and engage people in green energy. The EcoOptimize algorithm determines the ideal location and size for green energy projects to maximize energy production and environmental benefit. BioDiversityGuard utilizes machine learning to predict animal impacts from green energy projects. It emphasizes environmental safety. Community Empower shares the advantages of green energy projects fairly via social network analysis and collaboration. SEO techniques function better and have less impact on the bottom line and environment than outdated techniques. SEOTES is the most innovative and effective renewable energy improvement. This abstract discusses SEOTES's whole technique and its importance for tackling the energy crisis's biggest issues. It emphasizes how SEOTES may improve renewable energy project planning, implementation, and participation. © 2024 IEEE.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

An Intense Systematic Review of Mobile Agent Based Harmful Threats And Attack Detection Systems in IOT Networks

An Intense Systematic Review of Mobile Agent Based Harmful T...

引用

Advance Computing and Innovative technologies in Engineering (ICACITE), International Conference on

作者： M. Vigenesh G. Ravivarman Prabhdeep Singh Shaik Vaseem Akram Deepti Sharma Mohit Tiwari Department of CSE Karpagam Academy of Higher Education Coimbatore Department of EEE Karpagam Academy of Higher Education Coimbatore Tamil Nadu Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department of Management Uttaranchal University Dehradun Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

A breach is detected by an intrusion detection system (IDS) by analysing events that are deemed risky from a security standpoint in systems and networks. Due to a surge in occurrences involving network security, organisations must implement IDS for the protection of sensitive data. Both intrusions coming from outside a network and those coming from within a distributed network are challenging to identify. IDS should be in charge of processing a sizable volume of data without overwhelming networks and monitoring systems. Due to the flaws and restrictions in centralised IDS, mobile agents (MA) were developed. By identifying harmful activity, these agents can carry out specified actions. From earlier works of literature, it was deduced that most of the existing IDS based on MA are not significantly effective

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multimodal Neuroimaging for Early Alzheimer's detection: A Deep Learning Approach

Multimodal Neuroimaging for Early Alzheimer's detection: A D...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Gurpreet Singh Chhabra Abhishek Guru Bhawna Janghel Rajput Leelkanth Dewangan Suman Kumar Swarnkar Department of GITAM School of Technology Computer Science & Engineering GITAM Deemed to be University Visakhapatnam Inida Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering Rungta College of Engineering in Bhilai Durg.(C.G.) G H Raisoni Institute of Engineering and Business Management Jalgaon Maharashtra India Department of Computer Science & Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur Chhattisgarh India

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent

关键词：

来源：评论

学校读者我要写书评

暂无评论

Minimizing Age of Collection for Multiple Access in Wireless Industrial Internet of Things

arXiv

引用

arXiv 2023年

作者： Liang, Jiaxin Chan, Tse-Tin Pan, Haoyuan Computer Network and Protocol Research Laboratory Huawei Technologies Shenzhen518060 China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper investigates the information freshness of Industrial Internet of Things (IIoT) systems, where each IoT device makes a partial observation of a common target and transmits the information update to a central receiver to recover the complete observation. We consider the age of collection (AoC) performance as a measure of information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when all cooperative packets for a common observation are successfully received. Hence, effectively allocating wireless time-frequency resources among IoT devices to achieve a low average AoC at the central receiver is paramount. Three multiple access schemes are considered in this paper: time-division multiple access (TDMA) without retransmission, TDMA with retransmission, and frequency-division multiple access (FDMA). First, our theoretical analysis indicates that TDMA with retransmission outperforms the other two schemes in terms of average AoC. Subsequently, we implement information update systems based on the three schemes on software-defined radios. Experimental results demonstrate that considering the medium access control (MAC) overhead in practice, FDMA achieves a lower average AoC than TDMA with or without retransmission in the high signal-to-noise ratio (SNR) regime. In contrast, TDMA with retransmission provides a stable and relatively low average AoC over a wide SNR range, which is favorable for IIoT applications. Overall, we present a theoretical-plus-experimental investigation of AoC in IIoT information update systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Application of Soft Computing Techniques for Predictive Analytics in Financial Markets 6

Application of Soft Computing Techniques for Predictive Anal...

引用

6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bhavya, K.R. Dharmaraj, A. Pareek, Prakash Ashok Sathe, Manoj Tiwari, Mohit Manoharan, Geetha Dr. N.S.A.M First Grade College Commerce and Management Karnataka Bangalore India Karpagam Academy of Higher Education Coimbatore Department of Management Tamil Nadu India Vishnu Institute of Technology Department of ECE A.P. Bhimavaram India PES Modern Institute of Business Studies Pune India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India School of Business SR University Telanagana Warangal India

ISBN: (纸本)9798350304480

Successful applications of predictive analysis can be found in finance. For these goals, modern soft computing theories are applied. In contrast to other apps, financial applications have unique characteristics. Forecasting is crucial, particularly in the financial industry because it lowers expenses, which can increase revenues and help businesses win the competition. Due to inescapable changes and expansion in every aspect of life, almost every organization on earth is operating in an unpredictable environment. Forecasting becomes increasingly difficult as a result of these developments, which either directly or indirectly affect stock market values. The demand for trustworthy, economical, and efficient forecasting models is therefore great in order to reduce uncertainty as well as risk in investing in the stock market. Academics and information researchers have developed a variety of time series models for the most precise and perfect future prediction. Financial autoregressive time series models have produced precise prediction-capable predictive models like the autoregressive moving average and autoregressive integrated moving average. To predict the closing value of the BSE100 S&P Sensex record every week and every day, discrete wavelet change and wavelet denoising soft computing models are combined with autoregressive models in the continuing work. © 2023 IEEE.

关键词： finance forecasting prediction analysis stock market

来源：评论

学校读者我要写书评

暂无评论

SEES-QL: An Improved Scalable and Energy-Efficient Scheme for WSNs based on Lightweight Q-learning

SEES-QL: An Improved Scalable and Energy-Efficient Scheme fo...

引用

Advances in science and Engineering Technology International Conferences (ASET)

作者： Antar S. H. Abdul-Qawy Ali Idarous Adnan Mohammed Sultan Mohammed Narendra Khatri Amir Shikhli Anwar Jarndal Department of Mathematics and Computer Science Abdulrahman Al-Sumait University Zanzibar Tanzania Department of Computer Engineering Hodeidah University Al Hudaydah Yemen Department of Computer Science and Information Technology The State University of Zanzibar Zanzibar Tanzania Department of Electronic and Computer Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Mechatronics Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Sustainable Engineering Asset Management Research Group University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

The energy efficiency of battery-operated sensing devices in IoT is a critical research area that needs further exploration. This paper employs lightweight reinforcement learning to improve energy savings in large-scale heterogeneous WSNs. We introduce SEES-QL (a Scalable and Energy-Efficient Scheme based on Q-Learning), an enhanced version of the zonal SEES protocol, that addresses the issue of frequent data transmission by dynamically adjusting nodes' transmission cycles without the need for a predefined model. In SEES-QL, on/off periods of radio transceivers are regulated based on transmission history and reading importance of each node independently, positively affecting total energy consumption, traffic load, and overall system lifetime. Performance evaluation demonstrates that SEES-QL achieves significant advancements in energy savings and transmission count reduction, leading to a remarkable 41% increase in the overall system lifetime compared to the traditional SEES protocol.

关键词： Performance evaluation Wireless sensor networks Protocols Q-learning Radio transceivers Energy conservation Telecommunication traffic Energy efficiency Sensors Load modeling

来源：评论

学校读者我要写书评

暂无评论

Predicting the Impacts of the Environment on the Probability and Severity of Electrical Injuries by AIML

Predicting the Impacts of the Environment on the Probability...

引用

2023 International Conference on New Frontiers in Communication, Automation, management and Security, ICCAMS 2023

作者： Deshmukh, Araddhana Arvind Kumar, K Parish Venkata Kumar, B.Anjanee Patro, Pramoda Singh, Pavitar Parkash Ashokkumar, N. Marathwada Mitra Mandal College of Engineering Department of Artificial Intelligence and Data Science Pune India Department of Computer Applications Vijayawada India Dadi Institute of Engineering and Technology Electronics and Communication Engineering Visakhapatnam India Koneru Lakshmaiah Education Foundation Department of Mathematics Hyderabad India Lovely Professional University Department of Management Phagwara India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Ece Tirupati India

ISBN: (纸本)9798350317060

The acceleration of global warming and environmental degradation has been attributed to industrial growth and fossil fuel utilization. Consequently, this phenomenon has resulted in a rise in the frequency and intensity of natural disasters. Due to their predominantly outdoor installation, generators are subject to significant impacts. The occurrence of natural disasters has led to an increase in accidents associated with electrical equipment. This paper analyses statistical data on electrical fires, electric shock accidents, and electricity-related accidents caused by climate change in domestic settings to determine the associated risk *** this study, we propose a risk assessment model for electrical fires, explicitly focusing on every month. Our approach involves analysing data on electrical hazards in different regional areas, particularly metropolitan cities, considering climatic conditions such as temperature and humidity. Additionally, we aim to evaluate the accident risk rating associated with natural disasters, specifically those related to low and high-voltage equipment. In this study, a risk analysis model has been developed to assess electrical hazards in different regions and equipment types. The model presented herein serves as a foundational predictive tool. Hence, offering electrical security amenities in the forthcoming period is feasible by giving a risk estimation map delineating potential dangers specific to each geographical area and wiring category via web platforms or mobile applications utilizing the investigation data provided. Additionally, it is imperative to enhance the resilience and dependability of electrical components as a proactive measure against electrocute ions resulting from climate change-induced emergencies. © 2023 IEEE.

关键词： Global warming

来源：评论

学校读者我要写书评

暂无评论

A New MRA and AA based Algorithm for the Higher Order Reduction Uncertain Systems

A New MRA and AA based Algorithm for the Higher Order Reduct...

引用

International Conference on science Technology Engineering and management (ICONSTEM)

作者： Vaishali Gajendra Shende S. Raviraja Kavitha P H. Mickle Aancy M Jogendra Kumar Varsha Mittal Department of Information Science and Engineering East point college of Engineering and Technology Bangalore Karnataka India Department of Computer Studies Royal Research Foundation® Mysuru University of Garden City Karnataka India Department of Electrical and Electronics Eneineering R.MK. Engineerine College Chennai Tamil Nadu Department of Management Studies Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The MRA and AA algorithms are utilized in this paper to present a novel approach for the reduction of high-order uncertain systems. The development of the program makes use of both of these mathematical approaches. As a result of this creative approach, the Continuous SISO system might be reduced. Measurement mistakes brought on by variations in the environment have the potential to render the system ambiguous, in contrast to other systems that have been reported in the literature. This strategy, in contrast to the methods that were used in the prior iteration literature, has the potential to build a stable reduced order (r-o) model from a stable initial high order uncertain system. By avoiding many of the constraints of interval arithmetic, affine arithmetic is used. One of the problems is that there is not a solution that is unbounded in many of the key scenarios. Through the utilization of typical numerical examples derived from interval literature, the methodology was validated. Among the topics that we investigate are uncertain systems, lower order models, affine arithmetic, and the modified Routh approximation approach.

关键词： Uncertain systems Adaptation models Computational modeling Approximation algorithms Stability analysis Reduced order systems Numerical models

来源：评论

学校读者我要写书评

暂无评论

Designing a Mobile Payment-Driven Mobility-as-a-Service Architecture for Bus Transportation 27

Designing a Mobile Payment-Driven Mobility-as-a-Service Arch...

引用

27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Chen, Kuongho Kao, Tzu-Yuan Yeh, Kuo-Hui Department of Field Application Neweb Technologies Co. Ltd 8F No. 97 Nangang Rd. Sec. 3 Taipei115 Taiwan Department of Computer Science and Information Management Soochow University 56 Sec. 1 Kui-Yang St Taipei100 Taiwan Department of Information Management National Dong Hwa University No.1 Sec. 2 Da Hsueh Rd. Shoufeng Hualien974301 Taiwan

This paper proposes a comprehensive architecture for a Mobility-as-a-Service (MaaS) platform, focusing on service browsing, ticketing, and payment, with a specific emphasis on integrating MaaS into bus payment systems. The proposed architecture aims to enhance user experience and convenience in accessing and paying for various transportation services while providing support for MaaS startups, offering a novel approach to MaaS, addressing challenges related to service discovery, ticketing, and payment through a unified system. The proposed MaaS platform holds the potential to improve mobility, reduce congestion, and promote sustainable transportation. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Cashless transactions E-payment Electronic Data Capture (EDC) Integration MaaS Mobile payment Mobility-as-a-Service Payment architecture Public Transit QR codes Seamless mobility

来源：评论

学校读者我要写书评

暂无评论

Assessing the Ability of AI-Driven Natural Language Processing to Accurately Analyze Unstructured Text Data

Assessing the Ability of AI-Driven Natural Language Processi...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： M. Vigenesh RaviKumar V Vivek D. Patil Sharma Sonu Kumar Nyarik Geyi Subir Chattopadhyay Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic Intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement.

关键词：

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：