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Hybrid ConvLSTM with Attention for Precise Driver Behavior Classification 6

Hybrid ConvLSTM with Attention for Precise Driver Behavior C...

引用

6th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2023

作者： Mustafa, Ghulam Cho, Seong-Je Hwang, Youngsup Dept. of Computer Engineering Sun Moon University Asan-si Korea Republic of Dept. of Software Science Dankook University Yongin Korea Republic of

ISBN: (纸本)9798350331288

Driving behavior classification plays an important role in many real-world applications, including traffic accident prevention, driver safety, usage-based insurance, and optimizing ridesharing services. In this research, we introduce a novel approach called Hybrid ConvLSTM with Attention for Driver Behavior Classification (HCLA-DBC), designed to achieve precise and reliable driver behavior classification. Our main focus is to distinguish three fundamental driving behavior classes: normal, drowsy, and aggressive, which play a vital role in supporting advanced driver-assistance systems (ADAS). To evaluate the performance of our proposed models, we conducted experiments using the publicly available UAH-DriveSet dataset. The evaluation comprised six distinct models, encompassing two machine learning models (SVM and Gradient Boosting) and four deep learning models (CNN, MLP, LSTM, and HCLADBC). Our HCLA-DBC model outperformed all other models and previous results on the same data. On the test data, it achieved an accuracy of 94.12%, precision of 94.24%, recall score of 94.12%, F1-score of 94.12%, and a macro-average ROC AUC of 98.83. These exceptional outcomes underscore the efficacy of our hybridized approach and its potential to significantly enhance driver behavior classification. The HCLA-DBC model not only promises new possibilities for driver behavior analysis but also paves the way for elevated ADAS capabilities and safer driving experiences. © 2023 IEEE.

关键词： Support vector machines

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Comparison Between Gramian Angular Fields (GAF) and Markov Transition Field (MTF) Images Data by Using them as Input to the Deep Learning Neural Network Solar Flare Production Platform

Comparison Between Gramian Angular Fields (GAF) and Markov T...

引用

2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Nagem, Tarek A M Hamad Alshareef, Sohil Mohamed, Abdelhafid Gihedan, Akram Albargathe, Salma Libyan British University Dept. Computer System Design Benghazi Libya University of Benghazi Dept. Software Engineering Benghazi Libya College of Computer Technology Dept. Software Engineering Benghazi Libya Darna University Dept. Computer Science Algoba Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The prediction of solar storms from real-time satellites data is an essential to protect various aviation, power, and communication infrastructures. For this reason, current research interest is focusing on creating systems that can predict solar fare accurately. This paper combers Gramian Angular Fields (GAF) images and Markov Transition Field (MTF) images that have been generated by converting pre-flare Geostationary Operational Environmental Satellite (GOES) data from 2010 to 2016. After that, the deep learning neural network for the solar flare production platform accepts both MTF and GAF images as input to generate the predictions. Furthermore, this paper;investigated using MTF and GAF images spritely as input to the solar flare production platform. After that, the results of MTF and GAF images were compared using various prediction verification measures. © 2022 IEEE.

关键词： Forecasting

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Reliability-Based Design of Time-Varying Electricity Tariff 31

Reliability-Based Design of Time-Varying Electricity Tariff

引用

31st International Conference on Electrical engineering, ICEE 2023

作者： Rastegar, Mohammad Sarajpoor, Nima School of Electrical and Computer Engineering Shiraz University Shiraz Iran University of Calgary Dept. of Electrical and Software Eng. Calgary Canada

ISBN: (纸本)9798350312560

Implementing price-based energy management programs to change the consumption pattern of residential consumers has become an indispensable solution to the operational challenges of smart distribution power systems. However, practical methods to design efficient time-varying prices to improve the reliability and operation of distribution systems have remained scarce. This paper proposes a bi-objective optimization problem to design time-differentiated electricity tariff for residential energy management, where a new reliability evaluation method is employed to evaluate the reliability enhancement caused by load shifting. A set of numerical studies and sensitivity analysis are conducted to investigate the effectiveness of the proposed pricing method. © 2023 IEEE.

关键词： Energy management

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A Blockchain-Powered Loan Management System Enhanced with Smart Contract 1

A Blockchain-Powered Loan Management System Enhanced with Sm...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Saleem, Muhammad Haris, Muhammad Naeem, Mehwish Abro, Abdul Basit Ahmad, Maaz Ahad, Abdul Iqra University Main Campus Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium Indus University Dept. of Computer Science Karachi Pakistan Iqra University Main Campus Dept. of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

Most financial lending systems in use today are based on a single service platform, transactions are opaque, and the majority of system participants can be tracked back to Mechanisms for protecting data privacy are not strong enough to withstand cyberattacks. We suggest lending over the block chain (LoC), a cutting-edge finance and credit facility built on smart contracts on a block chain hyper ledger fabric, as a solution to these problems. We use the poverty level for Chinese loans as an example. We create a digital accounting model to transfer assets between decentralized and centralized ledgers and suggest smart contract closing and unlocking methods. To safeguard the privacy of data, we established digital signatures and oracles. The opening rule's and the chain rule's performance evaluation demonstrates the applicability of our system in actual loan situations. © 2024 IEEE.

关键词： Smart contract

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Online Trained Model of Tweets Expertise 10

Online Trained Model of Tweets Expertise

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Mozaidze, Elena Zuev, Sergei Kabalyants, Petr Belgorod State Technological University Named After V.G. Shukhov Dept. Of Computer Software And Automated Systems Belgorod Russia V.I. Vernadsky Crimean Federal University Dept. Of Computer Engineering And Modelling Simferopol Russia

ISBN: (纸本)9798350372939

The paper proposes a model for the examination of tweets (brief texts and messages) in Russian. The model is based on the authors' explorations on the identification of tweets meanings. The main idea is to apply the latest developed quantum machine learning method to the hierarchical classification models of texts. This approach avoids the text corpus necessity and supports the online learning due to the very design. The model proposed is tested in the experiment. The target content was given in the experiment and the model is used to measure the correspondence between the target topics and the test messages. The result of the experiment shows that the proposed method allows to distinguish the target content in the test text. © 2024 IEEE.

关键词： Expert systems

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Security and Threat Detection through Cloud-Based Wazuh Deployment 1

Security and Threat Detection through Cloud-Based Wazuh Depl...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Moiz, Syed Majid, Abdul Basit, Abdul Ebrahim, Mansoor Abro, Abdul Ahad Naeem, Mehwish Iqra University Dept. of Computer Science Karachi Pakistan Dept. of Cyber Security Karachi Pakistan Indus University Dept. of Computer Science Karachi Pakistan Iqra University Dept. of Software Engineering Karachi Pakistan University Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9798350373042

In today's interconnected business landscape, security is the cornerstone of all organizations, irrespective of their size or industry. This research emphasizes the critical need to safeguard organizational assets from cyber threats and highlights the pivotal role that firewalls play in fortifying network and cyber systems. The challenge lies in maintaining updated firewall defenses to prevent server downtime and the circumvention of security measures. To address these concerns, this study introduces a novel approach by integrating Cloud Wazuh, a user-friendly solution tailored for non-technical personnel, providing streamlined deployment and customizable security rules. Leveraging Wazuh suite of tools, particularly the Host-Based Intrusion Detection System (HIDS), the research detects and mitigates potential threats, including patterns associated with attacks such as Port Scanning, Denial of Service (DoS), and Metasploit. Additionally, Wazuh offers a range of features, from Security Analytics to File Integrity Monitoring, to comprehensively protect against cyber threats. While implementing Cloud Wazuh may entail initial costs, it proves cost-effective compared to maintaining a dedicated IT security team. This research underscores how Cloud Wazuh deployment serves as a pivotal tool for organizations looking to strengthen their security measures, safeguard their data, and ensure uninterrupted operations in an increasingly digital and interconnected world. © 2024 IEEE.

关键词： Cybersecurity

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Visualizing the Path of a Photo Taker from Image Metadata 4

Visualizing the Path of a Photo Taker from Image Metadata

引用

4th International Informatics and software engineering Conference, IISEC 2023

作者： Majid, Rasty Shakhawan Varol, Cihan Varol, Asaf Nasab, Ahad Firat University Department of Software Engineering Erbil Iraq Sam Houston State University Computer Science Department HuntsvilleTX United States College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (纸本)9798350318036

One of the most challenging investigations in forensic analysis is the work conducted on smartphones. Specifically using smartphone data to provide evidence for a case is becoming a norm nowadays. Many Android device applications use and work with location services that assist investigators in working with log files that store valuable information. Images captured on Android devices will save geolocation information, date, and time in the properties. In this work, a tool is developed to acquire the date and geolocation for each taken photo so that the path of the photo taker can be displayed on Google Maps. The work showed that the application can take about 4 and a half seconds to process four images and display the map to the investigator. © 2023 IEEE.

关键词： Metadata

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Multimodal Emotion Recognition using Deep Learning Architectures 2

Multimodal Emotion Recognition using Deep Learning Architect...

引用

2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Hina, Iram Shaukat, Arslan Akram, Muhammad Usman National University of Sciences and Technology College of EME Dept of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665498197

Emotions are an essential part of immaculate communication. The purpose of this research work is to classify six basic emotions of humans namely anger, disgust, fear, happiness, sadness and surprise. In proposed method a sequential deep convolutional neural network is proposed for audio and visual modality. Audio classification is performed via fine-tuning of a pre-trained AlexNet model whereas, visual classification is performed with a hybrid deep network containing CNN and LSTM. Decision level and score level fusion have been implemented for multimodalities. SVM, random forest, K-NN, and logistic regression classifiers were being used for classifying emotion for fused audio-visual data. Experiments have been performed on RML and BAUM-1s dataset with LOSO and LOSGO cross validation techniques respectively. Recognition rates were extremely positive which shows the validity of the proposed methodology. © 2022 IEEE.

关键词： Long short-term memory

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Faceswap Deepfakes Detection using Novel Multi-directional Hexadecimal Feature Descriptor 19

Faceswap Deepfakes Detection using Novel Multi-directional H...

引用

19th International Bhurban Conference on Applied Sciences and Technology, IBCAST 2022

作者： Qurat-Ul-Ain Javed, Ali Mahmood Malik, Khalid University of Engineering and Technology Dept. of Computer Science Taxila Pakistan University of Engineering and Technology Dept. of Software Engineering Taxila Pakistan Oakland University Dept. of Computer Science and Engineering United States

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

With the growing number of sophisticated deep learning algorithms and fake video generation applications, it is now possible to create highly realistic deepfake videos. Faceswap is the most commonly employed deepfakes approach, which is challenging to detect due to variations in the facial skin tone, illumination conditions, presence of accessories like glasses on the face, compression artifacts, etc. Existing local texture descriptors have achieved better performance on face recognition applications;however, they compute only the limited directional information while ignoring the magnitude details. This motivated us to develop a robust local texture descriptor to extract more directional and magnitude details from the adjacent pixels to effectively represent the video frames. For this purpose, we proposed a robust multi-directional hexadecimal feature descriptor (MDHFD) by combining the local hexadecimal pattern (LHeXDP) and Local Adjacent Neighborhood Magnitude Pattern (LANMP). LHeXDP calculates the orientation-based pattern by computing 1st- and 2nd-order derivatives at 0°, 45°, 90°, and 135° angles from each center pixel. LANMP computes the magnitude information from each central pixel to its adjacent pixels in horizontal, vertical, diagonal, and diagonal-back directions. Histograms of both the LHeXDP and LANMP are fused to compute a multi-directional feature vector, which is used to train a support vector machine to classify between the original or faceswap deepfakes video. We measured the performance of our system on the challenging faceswap subset of a diverse and large-scale Face Forensic++ and World Leaders datasets. Experimental results illustrate that the proposed method outperforms state-of-art methods for the detection of faceswap deepfakes. © 2022 IEEE.

关键词： Pixels

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CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms 5

CapsiScan: Autonomous Robotic System for Monitoring and Mana...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayakody, E.R.T. Rabel, W.T.T. Estrop, R.D.V. Herath, K.G.L.P. Harshanath, S.M.B. Abeygunawardhana, Pradeep K. W. Sri Lanka Institute Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9798350358131

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions. © 2023 IEEE.

关键词： Greenhouses

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