检索结果-内蒙古大学图书馆

Soil Image Classification Using Transfer Learning Approach: MobileNetV2 with CNN

SN computer science 2024年第1期5卷 199页

作者： Banoth, Ravi Kumar Murthy, B. V. Ramana Department of Computer Science and Engineering University College of Engineering Osmania University Telangana Hyderabad India Department of Computer Science and Engineering Stanley College of Engineering and Technology for Women Abids Telangana Hyderabad India

This paper presents a novel study on soil image classification, leveraging the synergistic potential of transfer learning and convolutional neural networks (CNNs). The proposed approach combines the strengths of the MobileNetV2 architecture with a customized CNN model for accurate and efficient soil type recognition. The pre-trained MobileNetV2 is used to capture generic features before fine-tuning it with a dedicated soil image dataset comprising four distinct classes: red, clay, black, and yellow soils. To enhance the model’s capacity for discerning intricate soil textures, a specially designed CNN architecture is incorporated. The model’s performance is rigorously evaluated on a dataset of 108 images, each sized at 256 × 256 pixels, achieving an exceptional accuracy rate of 100% on the test dataset. The promising results demonstrate the efficacy of the proposed methodology in soil image classification tasks, offering potential applications in precision agriculture, environmental monitoring, and land management. While these findings showcase remarkable accuracy, further investigations are recommended to assess the model’s generalization across diverse environmental conditions and an expanded range of soil image datasets. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Black soil Clay soil Convolutional neural networks (CNNs) MobileNetV2 Red soil Soil image classification Yellow soil

来源：评论

学校读者我要写书评

暂无评论

Probe Attack Detection Using an Improved Intrusion Detection System

引用

computers, Materials & Continua 2023年第3期74卷 4769-4784页

作者： Abdulaziz Almazyad Laila Halman Alaa Alsaeed Department of Computer Engineering College of Computer ScienceKing Saud UniversityRiyadh11421Saudi Arabia

The novel SoftwareDefined Networking(SDN)architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive ***,such benefits lead to a more adverse environment entailing network breakdown,systems paralysis,and online banking fraudulence and *** one of the most common and dangerous threats in SDN,probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities,which is thenmanipulated to undermine the entire ***,high performance,and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time,optimize prediction performance,and provide a holistic understanding of machine learning *** the extension of astute machine learning algorithms into an Intrusion Detection System(IDS)through SDN has garnered much scholarly attention within the past decade,this study recommended an effective IDS under the Grey-wolf optimizer(GWO)and Light Gradient Boosting Machine(Light-GBM)classifier for probe attack *** InSDN dataset was employed to train and test the proposed IDS,which is deemed to be a novel benchmarking dataset in *** proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 99.8%accuracy,99.7%recall,99.99%precision,and 99.8%F-measure.

关键词： GWO IDS InSDN LightGBM probe attack SDN

来源：评论

学校读者我要写书评

暂无评论

Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

来源：评论

学校读者我要写书评

暂无评论

Maxillary Sinus Disease Detection and Analysis Approaches in Deep Learning: Survey 1

Maxillary Sinus Disease Detection and Analysis Approaches in...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vimala, R. Preethi, D.M.D. Government College of Engineering Department of Computer Science and Engineering Bodinayakkanur Theni India Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India

ISBN: (纸本)9798350372748

The intend of this literature survey is to lessen the problems faced by dentists in the field of maxillary sinus diagnosis in image processing and to serve as a valuable reference to the literature related application. The odontogenic diseases may be diagnosed with atypical symptoms or it might mimic other conditions. This can create difficulty to disembark an accurate diagnosis. Sinusitis or temporomandibular joint disorder possibly is a symptom that resemble an odontogenic infection. Some odontogenic diseases may have overlapping symptoms, making it difficult to differentiate between them when based solely on clinical presentation. For instance, both a periapical abscess and a periodontal abscess can cause localized pain, swelling, and sensitivity. Diagnosing maxillary sinus issues through digital imaging, such as panoramic dental Xray, Cone Beam Computed Tomography (CBCT) and Computed Tomography (CT) scans, can be challenging due to the complex anatomy and the potential for overlapping structures. Radiologists utilize assorted computerized methods for maxillary sinus disease detection. CT scan analysis uses algorithms for segmentation and feature extraction, aiding machine learning algorithms in pattern recognition. CBCT provides detailed three-dimensional images, enabling comprehensive assessments of maxillary sinus anatomy and pathology. MRI utilizes signal intensity variations and texture analysis to identify potential diseases. Moreover, the integration of ultrasound, analysis of endoscopic video, and reporting of automated systems utilizing techniques of deep learning such as Convolutional Neural Networks and Recurrent Neural Networks, enhances precise detection by combining information from various imaging modalities. Interpreting dental radiographs can be complex, and certain conditions may not be clearly visible or may appear differently on different imaging modalities. It requires expertise and experience to accurately interpret radiographic findings and

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

引用

computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

来源：评论

学校读者我要写书评

暂无评论

Maximizing Learning Outcomes while Minimizing: Costs: A Cost-Effective Approach to M-Learning

International Journal of Interactive Mobile Technologies

引用

International Journal of Interactive Mobile Technologies 2024年第11期18卷 4-14页

作者： Suresh Kumar, B. Hemawathi, M.S. Dhanasekaran, P. Kaliappan, Vishnu Kumar Sivaganesan, D. Department of Electronics and Communication Engineering The Kavery Engineering College Tamil Nadu Salem India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India

In the realm of education, the pursuit of effective learning outcomes often faces the challenge of limited resources. This paper explores the intersection of maximizing learning outcomes and minimizing costs through a cost-effective approach to mobile learning (m-learning). Recognizing the ubiquitous presence of mobile devices and their potential to revolutionize learning, this study explores strategies that leverage the capabilities of mobile technology while being mindful of budgetary constraints. By leveraging the flexibility and accessibil- ity offered by m-learning platforms, educators can design engaging and interactive learning experiences that cater to diverse learners needs. The paper investigates various cost-effective methodologies, including utilizing open-source software, repurposing existing resources, and adopting collaborative learning environments. Furthermore, it examines the role of scalable technologies and adaptive learning algorithms in optimizing resource allocation and per- sonalizing learning experiences. Through case studies and empirical analysis, this research illustrates how institutions and educators can achieve significant cost savings without com- promising the quality of educational delivery. Ultimately, the findings highlight the transfor- mative potential of a cost-effective approach to m-learning in enhancing learning outcomes and widening access to education. © 2024 by the authors of this article.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

EzRenter -All In One Rental Platform with Blockchain-Powered Smart Contracts 5

EzRenter -All In One Rental Platform with Blockchain-Powered...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Rose, Able Maria Babu, K. Ouseph, Rosmy Ullas, Sallu Menon, Roshni R. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798350361155

This conference paper aims to create an innovative rental platform, acts as a bridge between users and providers. This platform transforms the rental landscape, offering a secure environment for seamless transactions through smart contract. It connects individuals and businesses with trustworthy owners, streamlining the rental process and improving user experiences. Unlike other platforms, this application spans diverse categories like tools, vehicles, electronics, clothing, real estate and more. Users enjoy a convenient and time-saving experience, eliminating the need to navigate multiple platforms. The vision is to transcend existing rental service limitations, providing an accessible and enjoyable rental experience for everyone. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Empowering Precision Agriculture: A Comprehensive Real-Time Monitoring and Decision Support System for Farmers 3

Empowering Precision Agriculture: A Comprehensive Real-Time ...

引用

3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Duraimurugan, N. Akalya, G. Aravind, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

A smart agricultural informatics platform integrated with Internet of Things (IoT) aims to revolutionize farming practices through a decentralized communication framework, the primary goal is to establish a knowledge-driven agricultural network in which farmers can discuss precision agronomy strategies, percentage know-how, and collaborate in real time to address challenges. The device carries a meteorological analytics module, powered by way of a RESTful Weather API, handing over a one-week climatic outlook to resource in dynamic choice-making. Advanced sensing technologies, including capacitive soil moisture sensors, electrochemical pH sensors, thermistors, and the DHT11 virtual sensor array, allow continuous tracking of soil physicochemical parameters and microclimatic situations for actionable insights. These features are wrapped in a mobility platform cell software program designed using the Flutter SDK, providing farmers with a modular UI/UX architecture that provides access to real-time farm diagnostics of their machines. Visualization of all facts using sensors. and predictive analytics for resources optimization Benefits of distributed computing and analytical aspects. When taken together this innovative platform helps farmers optimize spatial and temporal crop planning. Increase the sustainability of agricultural ecosystems and promote resilient, fact-driven agriculture. © 2025 IEEE.

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

Facial Emotion Based Automatic Music Recommender System

Facial Emotion Based Automatic Music Recommender System

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Singh, Mahesh Kumar Singh, Pushpendra Sharma, Amit ABES Engineering College Computer Science Department UP Ghaziabad India Ims Engineering College Information Technology Department UP Ghaziabad India

ISBN: (纸本)9798350329186

Listening to music can change the mood of any individual based on type of song like classic, pop or hip-hop. Music can have very strong influence on an individual's emotions. These emotions can be recognized by extracting various facial image features like Eye or Lip movement. Using facial expressions, meaning and context of words can be clearly understood by Humans. With the quick improvement of versatile systems and advanced interactive media innovations, advanced music has gotten to be the standard customer substance looked for by numerous youthful people. People frequently utilize music for mood regulation, more specifically to change a bad mood, to get motivated, to focus on their work, to increase energy levels or to reduce tension. Moreover, tuning in to the correct kind of music at the proper time may progress mental wellbeing. Recommendation of different songs based on an individual's current facial expressions could be latest research problem within the field of image processing. Very few researchers have proposed the solution of this research problem through automatic facial recognition using convolution neural network (CNN) models. In this paper, the proposed system based on CNN model focused on detecting human emotions from face image to identify list of songs to be played through music player to swing user's mood. The objective of this study is to improve the accuracy and reduce time and cost of proposed system for music recommendation. © 2023 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Shop-Easy: An Online Shopping Framework Emphasizing Vocal for Local Using Location and Review Based Services 1st

Shop-Easy: An Online Shopping Framework Emphasizing Vocal fo...

引用

1st International Conference on Advancements in Interdisciplinary Research, AIR 2022

作者： Jain, Aviral Tyagi, Divyaman Sharma, Shanu Department of Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (纸本)9783031237232

Nowadays the trend of online shopping is increasing day by day. A huge transformation and usage of online applications for shopping day-to-day essential items have been experienced during Covid-era. To further facilitate the online shopping industries, the local shopkeepers, and the customers, in this paper an efficient and organized online shopping platform is presented, which can be used by local shopkeepers to showcase their products digitally and by customers to easily manage and organize their day-to-day shopping-related tasks. With this proposed platform, the local regional shopkeepers can introduce their shops and products to nearby residents from time to time which makes their shop to be known by everyone, selling their products in an easier way and helps them in maintaining their ledger on a daily basis. Further, the proposed system will assist the customers by helping them in keeping track of their shopping lists timely and by choosing the best shop according to the location, item, and review-based notifications and reminders. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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：