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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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The Open Source Threat Intelligence Relational Dataset and Its Optimal Implementation

The Open Source Threat Intelligence Relational Dataset and I...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Yang, Yaru Li, Junyu Zhang, Ronghua Sah, Abhishek Pratap Yadav, Amit Khan, Asif Chengdu Neusoft University Department of Computer and Software Chengdu China University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China Charles Darwin University College of Engineering IT and Environment NT Australia Integral University Department of Computer Application Lucknow India

ISBN: (纸本)9798350361537

Threat intelligence provides a platform for cybersecurity engineers for attack traceability, which provides substantial knowledge database logs to defend against future security threats. Threat intelligence relationship extraction based on deep learning solves the challenge of threat knowledge construction to a certain extent but still faces problems such as lack of open-source datasets and the inability of the model to accurately correlate threat entities with potential relationships. Therefore, for cybersecurity research work, this paper designs a threat ontology, constructs the threat relationship dataset TreatRE by remote supervision, and opens this dataset. The dataset contains 12000 utterances and 12 threat relations from 500 CTIs, and it performs well in multiple relation models trained on deep learning methods. Meanwhile, we propose a multisensory attention-based threat intelligence relationship extraction method MAtt, which combines location perception, self-attention perception, and neuronal memory perception to further improve the threat relationship extraction effect. Experimental results show that the trained model based on TreatRE can more accurately extract the knowledge objects and their relationships described in threat intelligence. An accuracy score of 95.4% can be obtained using the MAtt method, which is 3.48% more than the best baseline compared with the same type of relationship extraction model. © 2024 IEEE.

关键词： Cybersecurity

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Design of data processing scheme of 5G terminal protocol conformance test system 4

Design of data processing scheme of 5G terminal protocol con...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Jingyao, Cao Yadav, Amit Khan, Asif Sah, Abhishek Pratap Chengdu Neusoft University School of Computer and Software Chengdu China Charles Darwin University College of Engineering It and Environment NT Australia Integral University Department of Computer Application Lucknow India University of Electronic Science and Technology of China School of Management Science and Engineering Chengdu China

ISBN: (纸本)9798350343670

Considering the fact that the test case runs abnormally and the test case execution fails during the development and debugging of the 5G terminal protocol conformance test system. Here useful approach is implemented by embedding data copy and storage module in the system, which completes the encapsulation, copy and storage of massive data generated during the operation of the system, so that equipment developers can correctly locate the errors in the system according to the data, and repair the system loopholes to ensure the stability and accuracy of the system. © 2024 IEEE.

关键词： 5G Terminal copy and storage module protocol conformance

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Fake News Recognition: A Machine Learning Approach for Text Analysis using Hyperparameter Tuning 3

Fake News Recognition: A Machine Learning Approach for Text ...

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3rd International Conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Singh, Neelam Shuaib, Mohd Rana, Mohit Maurya, Sudhanshu Negi, Harendra Singh Rawat, Vandana Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350343632

The internet has revolutionized communication and has come an ingrained part of people's lives, with a significant number of individualities exercising it for colorful purposes. Social media platforms have emerged as popular online destinations, allowing users to share posts and disseminate information. Unfortunately, these platforms often lack robust verification processes for users and their content. Consequently, some individuals exploit this vulnerability by spreading through these sites, bogus news. Such false information can be used as a tool for propaganda, targeting individuals, societies, organizations, or political parties. Given the overwhelming volume of online content, it becomes nearly impossible for humans to manually detect all instances of fake news. Therefore, there is a pressing need to develop classifiers for machine learning capable of automatically identifying and flagging fake news. This research study aims to explore the utilization of machine learning classifiers based on hyperparameter tuning for false news classification, shedding light on the various methodologies and techniques employed in this domain. © 2023 IEEE.

关键词： and Machine Learning Fake News Detection Hyperparameter Tuning Text Classification

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Hybrid Transformer-CNN Model for Automated Diagnosis of Pomegranate Fruit Diseases 10

Hybrid Transformer-CNN Model for Automated Diagnosis of Pome...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Tiwari, Raj Gaurang Misra, Alok Maheshwari, Himani Agarwal, Ambuj Kumar Sharma, Manish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University School of Computer Application Punjab Phagwara India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Department of Computer Science and Engineering Sharda School of Engineering and Technology India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350353778

Improving crop quality and output relies on early and precise disease diagnosis in pomegranate fruits. This study presents a new Feature Fusion Network for disease classification in pomegranate fruits, which combines Transformer and CNN modules. The proposed model classifies pomegranate fruit into five categories that are Healthy, Bacterial Blight, Anthracnose, Cercospora Fruit Spot, and Alternaria Fruit Spot. While the CNN module is great at collecting local visual aspects, the Transformer module is used to gather global contextual information. The model uses both global and local information for robust disease categorization by combining two complimentary feature representations. Adam, Adadelta, Gradient Descent, and RMSProp optimization algorithms are used to fine-tune the suggested Feature Fusion Network. After extensive training with Adam Optimizer, the suggested method attained a maximum accuracy of 96.45% in disease classification of pomegranate fruits. In addition, when the performance of the proposed model is compared with other popular deep learning architectures like ResNet, Inception, VGG, and MobileNet, it outperforms. © 2024 IEEE.

关键词： Fruits

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Applying Process Mining to Sensor Data in Smart Environment: A Comparative Study 7th

Applying Process Mining to Sensor Data in Smart Environment:...

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7th International Conference on Smart City applications, SCA 2022

作者： Iman, Elkodssi Laanaoui, My Driss Sbai, Hanae Mathematics Computer Science and Application Laboratory FST Mohammedia Hassan II University of Casablanca Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University of Marrakech Marrakech Morocco

ISBN: (纸本)9783031268519

Business process management is a popular approach that has caught the attention of several researchers over the years. BPM has been extended to Business Process Intelligence in order to add more intelligence to managing business processes. Process Mining is one of the emerging areas of Business Process Intelligence, which aims to automatically discover, verify and improve business processes. Nowadays, the emergence of smart environment. The data that occurs in this environment are more varied and are kind of unstructured data. In this sense, applying process mining, to analyze processes in a smart context is a challenging task, since the process mining techniques relies on a structured event log. In this paper, we present a comparative study of existing approaches on applying process mining techniques in smart BPM, as a first step towards developing a new approach to discover and analyze business processes from sensor data perspective. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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A Review on Classifications of Tracking Systems in Augmented Reality 1

A Review on Classifications of Tracking Systems in Augmented...

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1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021

作者： Bhakar, Suman Bhatt, Devershi Pallavi Dhaka, Vijaypal Singh Sarma, Yogender Kumar Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Application Manipal University Jaipur India

Augmented Reality (AR) is now becoming an exceptional technology that offers a new world. Users to enable their senses to feel, listen and see the surroundings in different and better ways witness a unique experience. AR technology is used to superimpose the real view of the user with the virtual scenes. This research work illustrates the core details of AR, its definition, history, and development process. In addition, discusses ideas having various approaches during utilization of AR frameworks along with glyph tracking system. Current applications of AR technology completely depend on the proper utilization of AR frameworks. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Augmented reality

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Toward a New Framework for Process-Aware IoT Discovery 7th

Toward a New Framework for Process-Aware IoT Discovery

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7th International Conference on Smart City applications, SCA 2022

作者： Elkodssi, Iman Laanaoui, My Driss Sbai, Hanae Mathematics Computer Science and Application Laboratory FST Mohammedia Hassan II University of Casablanca Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University of Marrakech Marrakesh Morocco

ISBN: (纸本)9783031268519

People’s lives have become more pleasant and uncomplicated in recent years as a result of recent technological advancements. Data sharing and connectivity across systems, devices, and people have been made easier with the introduction of IoT. In reality, the Internet of Things (IoT) provides a platform for ordinary items to become smarter, everyday communication to become more in-formed, and everyday processes to become more intelligent. In this regard, to manage business processes in the new IoT environments, many heterogeneous frameworks have been proposed. Since the number of IoT devices or things is increasing day by day and the number of services is also increasing, while dis-covering these IoT sensitive processes becomes a vital challenge and the traditional strategies of discovery of services are not efficient enough to handle this problem. The problem, approaches of discovering processes from sensor logs do not highlight IOT concepts in the output process models, which make process analysis and decision made more and more difficult. In this paper, we present a new process mining based approach to discover and analyze business process aware IoT. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms 15

A novel Approach for optimizing the crop yield by considerin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suganthavalli, K. Meenakshi, C. Parvathy, K. Department of Computer Science Chennai Tamil Nadu Pallavaram India Department of Computer Application Chennai Tamil Nadu Pallavaram India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350370249

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India's economy, which covers a significant 60.45% of the country's land and stands as the world's second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques. © 2024 IEEE.

关键词： Agriculture crop Prediction DL LSTM and RNN ML SVM

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Visual-semantic network: a visual and semantic enhanced model for gesture recognition

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Visual Intelligence 2023年第1期1卷 1-13页

作者： Wang, Yizhe Cao, Congqi Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an 710129 China

Gesture recognition has attracted considerable attention and made encouraging progress in recent years due to its great potential in applications. However, the spatial and temporal modeling in gesture recognition is still a problem to be solved. Specifically, existing works lack efficient temporal modeling and effective spatial attention capacity. To efficiently model temporal information, we first propose a long- and short-term temporal shift module (LS-TSM) that models the long-term and short-term temporal information simultaneously. Then, we propose a spatial attention module (SAM) that focuses on where the change primarily occurs to obtain effective spatial attention capacity. In addition, the semantic relationship among gestures is helpful in gesture recognition. However, this is usually neglected by previous works. Therefore, we propose a label relation module (LRM) that takes full advantage of the relationship among classes based on their labels’ semantic information. To explore the best form of LRM, we design four different semantic reconstruction methods to incorporate the semantic relationship information into the class label’s semantic space. We perform extensive ablation studies to analyze the best settings of each module. The best form of LRM is utilized to build our visual-semantic network (VS Network), which achieves the state-of-the-art performance on two gesture datasets, i.e., EgoGesture and NVGesture. © The Author(s) 2023.

关键词： Gesture recognition Semantic relationship Spatial attention Temporal modeling

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