检索结果-内蒙古大学图书馆

Recent Innovation in Smart and Sustainable technology (ICRISST), International Conference on

作者： Gokul Rangasamy Primya T Gurupriya Kumar Harinath Sivakumar Kanagaraj G Sathish Kumar D Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore Tamil Nadu India School of Computer Science Engineering and Information Technology Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

Nowadays, the majority of people rely heavily on the internet. Online transactions, online sign-ups, and online purchasing are used by everyone. One kind of threat to websites is phishing, which is an illegal activity that involves obtaining user data, including credit/debit card numbers, passwords, and login credentials. An efficient phishing detection method based on ensemble learning is presented in this study. The technique proposed, which combines three classifiers (K-Nearest Neighbours, KNN, Support Vector Machines, and Gradient Boosting Classifiers, GBC) to achieve the highest accuracy performance, has been shown to yield positive results overall (97.4%). The proposed technique uses feature extraction for processing the Uniform Resource Locator (URL) and predicts the website whether it’s phishing or not. Therefore, it is preferable and used to employ the suggested technique to identify phishing using machine learning.

关键词： Uniform resource locators Support vector machines Technological innovation Accuracy Phishing Nearest neighbor methods Boosting Feature extraction Prediction algorithms Ensemble learning

来源：评论

学校读者我要写书评

暂无评论

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

引用

22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Approach for Threat Detection in Cyber Security using AI algorithm 1

A Novel Hybrid Approach for Threat Detection in Cyber Securi...

引用

1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Gupta, Abhishek Kumar Dixit, Neetu Kumar, Sujeet Rawat, Priyanka Madhumita Jagannath International Management School Department of Information Technology New Delhi110070 India RBMI Department of Computer Application Greater Noida201306 India GL Bajaj Institute of Management Greater Noida201306 India Lingaya's Vidyapeeth Department of Computer Science & Engineering Faridabad121002 India

ISBN: (纸本)9798350353648

The dynamic cyber-attack landscape might require more traditional ways of detecting and preventing threats. It has also paved the way for new hybrid approaches that merge attributes of two or more different methods, enabling a better and more efficient cyber defence. Such an approach would be to apply artificial intelligence (AI) algorithms to detect threats. In this way, organizations can apply various capabilities to automatically scan network traffic round-the-clock for indicators of suspicious activities, notifying security teams immediately. Despite this, a system that relies entirely on AI algorithms gets many false positives, increasing the time and effort required by security personnel. Our hybrid approach includes human expertise and input to help detect potential risks. This comes as a human-in-the-loop solution, where AI algorithms identify suspicious signals, and humans then corroborate those leads before making an arrest. Combining human and AI intelligence results in a far more accurate and efficient threat detection system. Our strategy incorporates big data analytics and anomaly detection methods to highlight unusual behaviours that suggest a risk is forming. This allows our system to discover and eliminate these cyber attacks in real-time instead of merely reacting after a breach. By leveraging our novel hybrid approach, you can have better coverage and a more robust cyber security plan in the face of threats, giving you a higher level of protection and peace of mind about future attacks. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Collaborative Decision-Making for Adaptive Cybersecurity Policies 12th

Collaborative Decision-Making for Adaptive Cybersecurity Pol...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Deshmukh, Anand B. Chopade, Pallavi Devidas Yadav, Rajeev Chavhan, Gajanan H. Ingle, Pravin E. Ajani, Samir N. Department of Information Technology Sipna College of Engineering and Technology Amravati MS Amravati India Institute of Management and Entrepreneurship Development Pune411038 India Computer Science and Engineering Arya College of Engineering Jaipur India E&TC VIIT Pune India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Static cybersecurity policies aren’t working well enough to protect businesses from dynamic and complex attacks in today's fast-changing cyber threat environment. It is becoming clearer that we need cybersecurity policies that can change with new threats and changing business needs in order to deal with this problem. The skills and ideas of many people are needed to make cybersecurity protection more flexible and effective. This is why collaborative decision-making is so important for making and enforcing these kinds of rules. Collaboration for making decisions about changing hacking policies is looked at in great detail in this study. The article looks at how decision-making theories and the creation of cybersecurity policies relate to each other by looking at a lot of current literature, theoretical models, and real-life case studies. What are the ideas behind making decisions together? They are explained using hacking as an example. This shows how important it is for stakeholders to be involved, for people to be able to talk to each other, for trust to exist, and for the organization's culture to shape good policy results. This paper suggests a way to include joint decision-making principles in the creation of flexible cybersecurity policies by looking at examples of successful applications and problems that different organizations have had. To successfully deal with problems that change, this framework stresses the need for flexible decision-making, open lines of communication, and regularly updated partner involvement. This paper also looks at how new tools can help people make decisions together and make it easier to use rules that change over time. Finally, the paper talks about the problems and possible futures in this area, pointing out obstacles to acceptance and suggesting more study and work in this area. Organizations can be more resistant to cyber dangers and better adapt to changes in the security scene if they encourage stakeholders to work together

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

LSTM & MACD Approach: Prediction and Analysis of Stock Market

LSTM & MACD Approach: Prediction and Analysis of Stock Marke...

引用

Next Generation Communication & information Processing (INCIP), International Conference on

作者： L. Jerart Julus S.N. Syed Naweed D. Aadhavan S. Sudhakara Sankar R.S. Rampriya T. Kavitha Department of IT School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information System Vellore Institute of Technology Vellore India Department of ECE Vel Tech University Chennai Tamilnadu India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Investing and researchers alike have long been fascinated by the volatile and dynamic nature of financial markets. For the prediction and analysis of stock market movements, we present a hybrid approach that combines Long Short-Term Memory (LSTM) and Moving Average Convergence Divergence (MACD). Leveraging the power of deep learning and technical analysis, this hybrid model offers a comprehensive solution for forecasting and understanding market trends. The LSTM component, a type of recurrent neural network, provides the model with the ability to capture and learn intricate patterns in historical stock price data, thus enabling accurate short and long-term predictions. The MACD indicator, a widely used tool in technical analysis, supplements the LSTM by identifying potential reversal points and providing valuable insights into market momentum. A hybrid approach will provide a stronger and more reliable forecasting framework by combining both LSTM and MACD. To validate the model's efficacy, we apply it to historical stock market data and conduct a thorough analysis of its predictive performance. Our findings suggest that the Hybrid LSTM-MACD approach outperforms traditional methods and offers a promising solution for investors, traders, and financial analysts seeking to navigate the complex world of stock market trading.

关键词： Analytical models Accuracy Predictive models Market research Data models Internet Stock markets Forecasting Long short term memory Convergence

来源：评论

学校读者我要写书评

暂无评论

Deep Transfer Learning Approach for Facial and Verbal Disease Detection 3

Deep Transfer Learning Approach for Facial and Verbal Diseas...

引用

3rd International Conference on Advancements in Computing, ICAC 2021

作者： Manage, Dilki Madara Alahakoon, A. M. Ishan Shavinda Weerathunga, Kavindi Weeratunga, Thenuka Lunugalage, Dilani De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%. © 2021 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Academic Depression Detection Using Behavioral Aspects for Sri Lankan University Students 3

Academic Depression Detection Using Behavioral Aspects for S...

引用

3rd International Conference on Advancements in Computing, ICAC 2021

作者： Gamage, Madhuransi Agrahere Arachchi, Ranruwini Matara Naotunna, Sawandi Rubasinghe, Tharushi Silva, Chamari Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

Academic Depression is a widespread problem among undergraduate students in Sri Lanka. It is exhausting and has a detrimental impact on students' academic life. Therefore, the development of a technique to estimate the probability of depression among undergraduates is a blessed respite. Depression is mostly caused by a failure to check students' psychological well-being on a regular basis. Identifying depression at the college level, leading the students to get proper therapy treatments. If a counselor detects depression in a student early enough, he/she can successfully assist the student in overcoming depression. However, keeping track of the substantial changes that occur in students because of depression becomes challenging for the counselor with a considerable number of undergraduates. The advancement of image processing and machine learning fields has contributed to the creation of effective algorithms capable of identifying depression probability. Depression Possibility Detection Tool (DPDT) is considered an effective automated tool that brings the depression probability of a certain student. In DPDT, the result is generated by concerning four main strategies. They are facial expressions, eye movements, behavior changes (step count and phone usage), and physical conditions (heart rate and sleep rate). Convolutional Neural Network (CNN) with Visual Geometry Group 16 (VGG16) model, Residual Neural Network (ResNet-50), Random Forest (RF) classifier is the main models and techniques used in the system. More than 93% of accuracy was generated in every trained model. The paper concludes the system overview along with four strategies, literature review, methodologies, conclusion, and future works. © 2021 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Repairing TSVs for 3D ICs Using Redundant TSV 1

引用

International Conference on Industrial Instrumentation and Control,ICI2C 2021

作者： Ghosh, Sudeep Banik, Mandira Chakraborty, Tridib Mizan, Chowdhury Md. Ghosh, Trishita Basu, Soumyadipta Department of Information Technology Guru Nanak Institute of Technology Sodepur Kolkata700114 India Department of Computer Science and Engineering Guru Nanak Institute of Technology Sodepur Kolkata700114 India

ISBN: (数字)9789811670114

ISBN: (纸本)9789811670107

The manufacturing defects of through silicon via TSVs-based three-dimensional integrated circuit (3D IC) are one of the major problems in semiconductor industry. Shortcoming lenient 3D IC dependent on TSV is an arising space of semiconductor industry. As 3D IC is gaining significant importance because of shrinking size, increasing integration density and interconnection delay of modern devices, so it is necessary to repair TSVs faults occur during various manufacturing processes. An effect method would be the use of redundant TSV to repair the faulty functional TSV and also, we can enhance the reliability of a chips. Most of the existing solutions cannot guarantee to repair clustered defects when TSVs are dispersed non-consistently everywhere on the chip. Our fundamental point is to fix, however, much as could reasonably be expected sequential TSVs deficiencies. In this paper, we have proposed a method to make interconnection between functional TSVs and direct association between functional and redundant TSVs utilizing multiplexors (MUXs) in such way that necessary number of MUXs will least, reliance will be greatest, and furthermore can repair successive flaws. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Repair

来源：评论

学校读者我要写书评

暂无评论

Enhanced Fourier Transform Using Wavelet Packet Decomposition

引用

Journal of Sensor technology 2024年第1期14卷 1-15页

作者： Wouladje Cabrel Golden Tendekai Mumanikidzwa Jianguo Shen Yutong Yan Department of Physics and Electronic Information Engineering Optical Engineering Zhejiang Normal University Jinhua China Department of Computer Science and Technology Software Engineering Zhejiang Normal University Jinhua China Department of Physics and Electronic Information Engineering Electronic Engineering Zhejiang Normal University Jinhua China

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

关键词： Fourier Transform Wavelet Packet Decomposition Time-Frequency Analysis Non-Stationary Signals

来源：评论

学校读者我要写书评

暂无评论

Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model. 6

Unleashing Facial Expression Recognition for Stress Detectio...

引用

6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Kumar, Geethu S. Cheriyan, Jo Aparna, N. Swathy, J. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamilnadu Chennai600 119 India Department of Information Science and Engineering Cambridge Institute of Technology Karnataka Bangalore560 036 India Department of Computer Science and Engineering Saintgits College of Engineering Kerala Kottayam686 532 India Department of Computer Science and Engineering Cambridge Institute of Technology Karnataka Bangalore560 036 India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Deep learning

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：