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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Mohammad, Mohammad Mohammed, Aleem Senior Lecturer and Discipline Leader Software Engineering Melbourne Institute of Technology Sydney Campus Sydney Australia Computer Science Engineering Melbourne Institute of Technology Sydney Australia

ISBN: (纸本)9789811986680

Over hundreds of years, Australia is well known with bushfire incidents, especially during summer, which are widely spread across the country. This has impacted badly and contributed in shaping an environment in a significant way. Eastern Australia is now considered as top most dangerous fire-zone in the world, and its predominant eucalyptus forests have evolved to thrive on the phenomenon of bushfire. Since 1851 approximately, 800 people in Australia and billions of animals have been killed because of the bushfires. The advancement of technology and the Internet revolution has made, researchers think about how to implement fire detection system using intelligent devices to protect Australian communities from bushfires. This paper aims in proposing a solution, which detects a fire using IoT technology concept by connecting with cloud-based environment and physical hardware components such as a Raspberry Pi, sensors to detect fire/temperature, and drones to detect the bushfire. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Extracting Driving Styles from Automotive Sensor Data to Develop Personas 9

Extracting Driving Styles from Automotive Sensor Data to Dev...

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9th International Conference on computer science and engineering, UBMK 2024

作者： Kaya, M. Cagri Bouraffa, Tayssir Wnuk, Krzysztof Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering GothenburgSE-41296 Sweden Blekinge Institute of Technology Department of Software Engineering KarlskronaSE-37179 Sweden

ISBN: (纸本)9798350365887

Creating accurate and insightful personas for automotive applications requires understanding the diverse driving styles that emerge from sensor data. This paper introduces initial results of our methodology for developing personas based on the analysis of automotive sensor data, which captures various driving behaviors. We present a comprehensive process that begins with the extraction of driving events from sensor data, followed by the identification of distinct driving styles through K-Means clustering. Our approach is innovative in its use of dynamic, real-world driving data as opposed to static or direct user interaction data commonly employed in persona development. This allows for a deeper understanding of driver behaviors, which are indirectly inferred from sensor data, thus providing a foundation for creating detailed personas. © 2024 IEEE.

关键词： Automotive sensor data clustering driving styles event detection persona development

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An Efficient Secure Routing and Hierarchical Approach to Elliptic Curve Cryptography Combined with Distributed Group Key Management 9

An Efficient Secure Routing and Hierarchical Approach to Ell...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Anitha, Cuddapah Priscila, S. Silvia Mahaveerakannan, R. Balakrishnan, Biju Mohan Babu University Computer Science and Systems Engineering SreeVidyaniketan Engineering College AP 517102 India Department of Computer Science Tamil Nadu India Saveetha School of Engineering SIMATS Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

Applications for such groupings are numerous, as is the need for reliable and secure internal communication. To ensure multicast application security, a group key must be updated each time a member joins or departs the group. Approximately ten years ago, many effective management techniques were implemented to reduce computer overheads. They had not, however, been able to improve the efficiency of rekeying. Elliptic Curve Cryptography for Effective Group Key Management (ECC) is offered for efficient and group communication that is secure in dynamic multicast groups. This strategy creates a hierarchical arrangement among group members. The key server uses each group's position within the hierarchy private key to create a public key. In this research, we will describe a method for managing group keys that is dispersed and hierarchicalsecures the creation and distribution of group keys using elliptic curve cryptography and the hash function. These freshly obtained public keys are used the creation of a shared group key. The key server then provides a group member with the group key and the private key's inverse value. The group key is updated when a new member joins, and the subgroup members are informed of the modified keys. The main server also updates the other group members of the inverse value of the departing member. As a result, this process will make rekeying less computationally demanding. The suggested method reduces computing overhead and communication overhead according to simulation results. © 2023 IEEE.

关键词： Inverse problems

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Adaptive design and implementation of automatic modulation recognition accelerator

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 779-795页

作者： Wang, Bin Wei, Xianglin Wang, Chao Li, Junnan Jiao, Xiang Fan, Jianhua Li, Peng School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China 63rd Research Institute National University of Defense Technology Nanjing210007 China

Automatic modulation recognition-oriented Deep Neural Networks (ADNNs) have achieved higher recognition accuracy than traditional methods with less labor overhead. However, their high computation complexity usually far exceeds the computation capacity of communication devices built on Field Programmable Gate Array (FPGA) platform. When solving the problem of insufficient resources, the complete operation of FPGA-based accelerator can be achieved by dividing the calculation into several parts and calculating them separately, but this will cause unaccepTable latency. In this backdrop, we develop a new ADNN model, named VT-CNN2+, to promote the recognition accuracy. Then, after stating the resources and latency problems for implementing VT-CNN2+ on the FPGA platform, an adaptive hardware accelerator is put forward. To implement the accelerator, Area folding is introduced to optimize resources consumption. Moreover, Literacy Optimization, Parallelism Optimization, Inter-layer Cascading, Temporary Cache and Data Loading Optimization are adopted to reduce latency. Afterwords, the two components in our accelerator are detailed, i.e., Once-designed module and Re-designed module. Finally, to evaluate the performance and adaptivity of our accelerator, a series of experiments are conducted on two different FPGA platforms, i.e., AX7350 and ZedBoard. Results show that our accelerator can successfully adapt to different FPGA platforms and it can remarkably reduce the processing latency. Moreover, our accelerator’s processing speed of 0.066249s per single data sample with much lower energy consumption is one order of magnitude faster than desktop-level Central Processing Units (CPUs), two orders of magnitude faster than embedded CPUs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep neural networks

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EEG emotion recognition framework based on invariant wavelet scattering convolution network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2181-2199页

作者： Elrefaiy, Ahmed Tawfik, Nahed Zayed, Nourhan Elhenawy, Ibrahim Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt Computers and Systems Department Electronics Research Institute Cairo Egypt Mechanical Engineering Department The British University in Egypt Cairo Egypt

EEG signals for real-time emotion identification are crucial for affective computing and human-computer interaction. The current emotion recognition models, which rely on a small number of emotion classes and stimuli like music and images in controlled lab conditions, have poor ecological validity. Furthermore, identifying relevant EEG signal features is crucial for efficient emotion identification. According to the complexity, non-stationarity, and variation nature of EEG signals, which make it challenging to identify relevant features to categorize and identify emotions, a novel approach for feature extraction and classification concerning EEG signals is suggested based on invariant wavelet scattering transform (WST) and support vector machine algorithm (SVM). The WST is a new time-frequency domain equivalent to a deep convolutional network. It produces scattering feature matrix representations that are stable against time-warping deformations, noise-resistant, and time-shift invariant existing in EEG signals. So, small, difficult-to-measure variations in the amplitude and duration of EEG signals can be captured. As a result, it addresses the limitations of the previous feature extraction approaches, which are unstable and sensitive to time-shift variations. In this paper, the zero, first, and second order features from DEAP datasets are obtained by performing the WST with two deep layers. Then, the PCA method is used for dimensionality reduction. Finally, the extracted features are fed as inputs for different classifiers. In the classification step, the SVM classifier is utilized with different classification algorithms such as k-nearest neighbours (KNN), random forest (RF), and AdaBoost classifier. This research employs a principal component analysis (PCA) approach to reduce the high dimensionality of scattering characteristics and increase the computational efficiency of our classifiers. The proposed method is performed across four different emotional classific

关键词： Support vector machines

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Triple Diseases Prediction System using Deep Learning Techniques

Triple Diseases Prediction System using Deep Learning Techni...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： R. Vijayan V. Mareeswari A. B. Jaswanth Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu Department of Software and Systems School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu MTech (Software Engineering) School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore Tamilnadu

With the existing deep learning models in predicting multiple diseases primarily focus on analyzing individual diseases in isolation, lacking a unified system for multi-disease prediction. This project presents an approach to predict multiple diseases using Flask API, with a specific focus on brain tumors, COVID-19 and pneumonia. The proposed work represents a significant contribution to the field of disease prediction, harnessing the power of deep learning algorithms and modern web application development. The primary focus is on disease prediction, with a particular emphasis on ensuring accuracy and accessibility for end-users. The initial phase of this research involves data collection, where relevant datasets of various diseases are gathered. These datasets serve as the foundation for training and validating the deep learning models. Two prominent deep learning algorithms, Sequential CNN and VGG16, are employed for this purpose. These algorithms are chosen for their ability to handle complex data and recognize patterns within medical images and other health-related data. The core of the research involves training the deep learning models using the collected datasets. This step is crucial in enabling the models to learn and generalize from the provided data, ultimately enhancing their predictive capabilities. The models are modified to elevate their performance and accuracy. Following the training phase, the models are rigorously tested to evaluate their predictive accuracy. This assessment is vital in gauging the real-world applicability of the models in medical diagnosis. To make these powerful disease prediction models accessible to a wider audience, a front-end web application is developed.

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A Comprehensive Survey on Summarization Techniques

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SN computer science 2023年第5期4卷 560页

作者： Uppalapati, Padma Jyothi Dabbiru, Madhavi Rao, K. Venkata Research Scholar Computer Science and Systems Engineering Department AU-TDR Hub Andhra University Andhra Pradesh Visakhapatnam 530003 India Computer Science and Engineering Department Dr.Lankapalli Bullayya College of Engineering Andhra Pradesh Visakhapatnam 530013 India Computer Science and Systems Engineering Department Andhra University Andhra Pradesh Visakhapatnam 530003 India Computer Science and Engineering Department Vishnu Institute of Technology Andhra Pradesh Bhimavaram 534202 India

Summarization is the method of extracting valuable information or a summary out of a significant amount of data using a variety of computation techniques. The summarization task has been studied for a long time. Researchers are still paying attention to this task, as text can be extracted from a variety of formats, including text, audio, and multimedia, and because it presents a number of special challenges that require deep learning techniques, including temporal ordering, compression ratio, redundancy, cohesion, and coreference. In addition, the study discusses numerous cutting-edge datasets that can be used for text summarizing as well as various assessment metrics that are used to this issue. The paper’s primary goal is to summarize the various deep learning techniques currently being utilized and addresses the challenges of summarization in the education sector. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Abstractive text summarization (ATS) Corpora Deep learning Machine learning Natural language processing (NLP) Summary evaluation metrics

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Explainable AI for Healthcare: An Approach Towards Interpretable Healthcare Models 24

Explainable AI for Healthcare: An Approach Towards Interpret...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Zahoor, Kanwal Bawany, Narmeen Zakaria Ghani, Usman Jinnah University for Women Institute of Business Administration Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Artificial intelligence (AI) along with deep learning techniques has become an integral part of almost all aspects of life. One of the domains significantly impacted by this technological revolution is healthcare. Deep learning-based AI systems assist clinicians and medical professionals in disease diagnosis, personalized treatment, and monitoring through wearables, among other applications. Despite its expedient integration in healthcare, the trustworthiness of deep learning models remains a concern, primarily due to a lack of understanding of their underlying processes. However, Explainable AI (XAI) offers explanations through various methods, including Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive explanation (SHAP), and GRAD-CAM. XAI is utilized to enhance transparency, allowing users to understand and trust AI decisions. In this study, we present deep learning models for the classification of pneumonia disease in Chest X-ray Images followed by their explanations. Convolutional Neural Networks (CNNs) and other pre-trained models, including VGG16, MobileNetV3, and ResNet50, were used for classification of images as 'normal' or 'pneumonia'. The VGG16 model, known for its exceptional image understanding capabilities, achieved the highest accuracy, with an impressive 93% score. Further, we used XAI techniques including SHAP, LIME, and Grad-CAM for explanation of models. LIME and Grad-CAM provided more accurate results than SHAP in our experiments. This approach was taken to evaluate the fairness and transparency of the model. The insights gained from XAI can be used to refine and improve machine learning models by identifying areas of weakness or misinterpretation which increases overall model robustness. © 2023 IEEE.

关键词： Deep learning Explainable AI Grad-CAM Healthcare LIME SHAP Transfer learning

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Phase Re-Service in Reinforcement Learning Traffic Signal Control

Phase Re-Service in Reinforcement Learning Traffic Signal Co...

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International Conference on Intelligent Transportation

作者： Zhiyao Zhang George Gunter Marcos Quinones-Grueiro Yuhang Zhang William Barbour Gautam Biswas Daniel Work Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University USA Institute for Software Integrated Systems Vanderbilt University USA Department of Computer Science Institute for Software Integrated Systems Vanderbilt University USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

This article proposes a novel approach to traffic signal control that combines phase re-service with reinforcement learning (RL). The RL agent directly determines the duration of the next phase in a pre-defined sequence. Before the RL agent's decision is executed, we use the shock wave theory to estimate queue expansion at the designated movement allowed for re-service and decide if phase re-service is necessary. If necessary, a temporary phase re-service is inserted before the next regular phase. We formulate the RL problem as a semi-Markov decision process (SMDP) and solve it with proximal policy optimization (PPO). We conducted a series of experiments that showed significant improvements thanks to the introduction of phase re-service. Vehicle delays are reduced by up to 29.95% of the average and up to 59.21% of the standard deviation. The number of stops is reduced by 26.05% on average with 45.77% less standard deviation.

关键词： Shock waves Reinforcement learning Delays Logic Standards Queueing analysis Optimization Intelligent transportation systems

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Earthworm Optimization with Improved SqueezeNet Enabled Facial Expression Recognition Model

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computer systems science & engineering 2023年第8期46卷 2247-2262页

作者： N.Sharmili Saud Yonbawi Sultan Alahmari E.Laxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa Computer Science and Engineering Department Gayatri Vidya Parishad College of Engineering for WomenVisakhapatnamAndhra PradeshIndia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Facial expression recognition(FER)remains a hot research area among computer vision researchers and still becomes a challenge because of high intraclass *** techniques for this problem depend on hand-crafted features,namely,LBP,SIFT,and HOG,along with that a classifier trained on a database of videos or *** execute perform well on image datasets captured in a controlled condition;however not perform well in the more challenging dataset,which has partial faces and image ***,many studies presented an endwise structure for facial expression recognition by utilizing DL ***,this study develops an earthworm optimization with an improved SqueezeNet-based FER(EWOISN-FER)*** presented EWOISN-FER model primarily applies the contrast-limited adaptive histogram equalization(CLAHE)technique as a pre-processing *** addition,the improved SqueezeNet model is exploited to derive an optimal set of feature vectors,and the hyperparameter tuning process is performed by the stochastic gradient boosting(SGB)***,EWO with sparse autoencoder(SAE)is employed for the FER process,and the EWO algorithm appropriately chooses the SAE ***-ranging experimental analysis is carried out to examine the performance of the proposed *** experimental outcomes indicate the supremacy of the presented EWOISN-FER technique.

关键词： Facial expression recognition deep learning computer vision earthworm optimization hyperparameter optimization

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