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IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Akash Das K. M. Asieb Hasan Dept. of Computer Science Federation University Australia Ballarat Australia Software Engineer Glichin’ Dhaka Bangladesh

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

This study explores the application of machine learning techniques to predict rainfall in Australia, addressing the challenges posed by the country’s highly variable climate. Utilizing a comprehensive dataset spanning approximately 10 years of daily weather observations from multiple Australian locations, we implemented and compared six machine learning models: Random Forest, XGBoost, Logistic Regression, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) Classifier, and Naive Bayes. The models were evaluated on their ability to predict the binary outcome of rainfall occurrence for the following day. Our results demonstrate that ensemble methods, specifically Random Forest and XGBoost, achieved the highest accuracy at 86%, closely followed by Logistic Regression at 85%. All models showed stronger performance in predicting non-rainy days compared to rainy days, reflecting the dataset’s inherent class imbalance. The ANN Classifier exhibited the highest recall for rain prediction, suggesting its potential utility in identifying possible rainfall events.

关键词： Logistic regression Rain Machine learning algorithms Artificial neural networks Predictive models Nearest neighbor methods Prediction algorithms Australia Random forests Meteorology

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Enhancing Video Action Recognition with Spatial–Temporal Convolutional Attention Networks 15th

Enhancing Video Action Recognition with Spatial–Temporal Co...

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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Kumar, Pramod Quraishi, Aadam Rusho, Maher Ali Raparthi, Mohan Degadwala, Sheshang Computer Science and Technology Ganga Institute of Technology and Management Haryana Jhajjar Kablana124104 India Intervention Treatment Institute HoustonTX United States Degree Program University of Colorado Boulder CO United States Software Engineer Alphabet Life Science DallasTX75063 United States Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9789819786305

Effectively harnessing feature correlations is crucial for optimal performance in video action recognition tasks, whether in spatial or temporal dimensions. Convolutional operations excel at capturing local features through correlations among nearby points, while self-attention mechanisms capture global information by enabling interactions among all feature points. However, the limitations of a single convolutional layer in comprehending holistic feature correlations and the tendency of self-attention layers to overlook local positional characteristics necessitate an innovative approach. We introduce the Spatial–Temporal Convolutional Attention Network, comprising the Spatial Convolutional Attention Network and the Temporal Convolutional Attention Network. This approach strategically combines the strengths of self-attention for global context and convolution for local features, mitigating the limitations of individual layers. Key innovations include [explicitly state innovations]. To reinforce the uniqueness of our approach, we provide specific comparisons with existing methods, highlighting its superiority. In rigorous experiments, our proposed network demonstrates enhanced recognition performance, quantified through metrics such as accuracy and F1 score improvements over baseline models. By effectively capturing feature correlations, our approach elevates the neural network's capacity for spatiotemporal modeling, marking a significant advancement in video action recognition. Elevating the neural network's capacity for spatiotemporal modeling. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Leveraging Blockchain-as-a-Certificate Authority for Authentication in 6G-Enabled Spatial Crowdsourcing Drone Services 100

Leveraging Blockchain-as-a-Certificate Authority for Authent...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Akram, Junaid Anaissi, Ali Sidana, Sagar Kumar, Deepak Jhaveri, Rutvij H. The University of Sydney School of Computer Science CamperdownNSW2008 Australia University of Technology Sydney Td School UltimoNSW2007 Australia McKinsey & Company Principal Software Engineer Dallas United States National Institute of Technology Meghalaya Department of Computer Science and Engineering India Pandit Deendayal Energy University School of Technology India

ISBN: (纸本)9798331517786

The integration of the Internet of Drone Things (IoDT) with spatial crowdsourcing, enhanced by 6G technology, has revolutionized environmental monitoring, particularly in managing Australian bushfires. This approach leverages drones' mobility, multidimensional motion, and ease of deployment to gather real-time data from hazardous or inaccessible areas. However, the unsecured wireless communication channels and limited computational resources of drones in typical IoDT scenarios make them susceptible to cyber-attacks, including spoofing, GPS manipulation, impersonation, man-in-the-middle, and hijacking. To counter these threats, we propose a robust security protocol that utilizes blockchain technology augmented by Hyperelliptic Curve Cryptography (HECC). By employing blockchain as a Certificate Authority (CA) and treating transactions as certifications, our framework, DronCert, eliminates the need for traditional CAs or Trusted Third Parties (TTP). This decentralized approach, combined with the high-speed, low-latency capabilities of 6G, significantly enhances data transmission security within the IoDT network. A comprehensive security analysis demonstrates DronCert's resilience against various attacks, such as Denial-of-Service (DoS), man-in-the-middle, replay, and unauthorized device representation. © 2024 IEEE.

关键词： Drones

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Application of the Servqual Model in the E-cosmetics Sector 24th

Application of the Servqual Model in the E-cosmetics Sector

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24th International Symposium for Production Research, ISPR 2024

作者： Köse, Nilüfer Zeren, Belkis İclal Çiçeklidağ, Paşa Gürbüzer, Selman Süha Sahin, Merve Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031835827

This study utilizes the SERVQUAL model to conduct a comprehensive analysis of the impact of service quality on customer satisfaction and loyalty within Türkiye’s rapidly expanding e-cosmetics sector. As the industry experiences substantial growth due to digital transformation and changing consumer behaviors, the importance of high service quality in maintaining customer loyalty has never been more critical. This research collected data from 95 participants through meticulously designed online surveys conducted between May and June 2024, employing an adapted SERVQUAL survey to assess service quality across five key dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Utilizing advanced statistical methods with SmartPLS 4.0, the study explores both the direct and indirect effects of these dimensions on customer satisfaction (CS) and customer loyalty (CL). The findings reveal that while tangibles, reliability, and assurance have strong positive impacts on customer satisfaction, other factors such as product quality, brand image, and user interface design also significantly affect customer satisfaction and loyalty. This study provides actionable insights into how e-cosmetics companies can enhance their service quality to better meet consumer expectations, thereby fostering sustained customer satisfaction and loyalty. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User experience

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WDBM: Weighted Deep Forest Model Based Bearing Fault Diagnosis Method

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computers, Materials & Continua 2022年第9期72卷 4741-4754页

作者： Letao Gao Xiaoming Wang Tao Wang Mengyu Chang Department of Computer Science City University of Hong KongHong Kong999077China School of Computer and Software Engineer Xihua UniversityChengdu610039China Nanjing University of Aeronautics and Astronautics Nanjing210008China McGill University MontrealH3G 1Y2Canada

In the research field of bearing fault diagnosis,classical deep learning models have the problems of too many parameters and high computing *** addition,the classical deep learning models are not effective in the scenario of small *** recent years,deep forest is proposed,which has less hyper parameters and adaptive depth of deep *** addition,weighted deep forest(WDF)is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision *** this paper,weighted deep forest model-based bearing fault diagnosis method(WDBM)is *** WDBM is regard as a novel bearing fault diagnosis method,which not only inherits the WDF’s advantages-strong robustness,good generalization,less parameters,faster convergence speed and so on,but also realizes effective diagnosis with high precision and low cost under the condition of small *** verify the performance of the WDBM,experiments are carried out on Case Western Reserve University bearing data set(CWRU).Experiments results demonstrate that WDBM can achieve comparative recognition accuracy,with less computational overhead and faster convergence speed.

关键词： Deep forest bearing fault diagnosis weights

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Social Media Multi-Emotion Detection 2

Social Media Multi-Emotion Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 IEEE.

关键词： Tweets

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Enhancing Deep Convolutional Neural Networks to Detect Pedestrians Effectively in Various Urban Environments

Enhancing Deep Convolutional Neural Networks to Detect Pedes...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md. Mehedi Hasan Nakib Aman Md. Wadud Jahan Software Engineer I Developer eXperience Hub Rajshahi Bangladesh Department of Computer Science and Engineering Pabna University of Science and Technology Pabna Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

In computer vision, accurately identifying pedestrians is a crucial difficulty, especially in applications where safety is of the highest priority, such autonomous driving and urban planning. Deep learning has made progress, but challenges such changing conditions, barriers, and processing power requirements still exist. In this paper, we present an improved deep convolutional neural network (DCNN) designed with pedestrian identification in mind. Improving accuracy in various settings while maximizing processing efficiency is our goal. We carefully prepare the data and create a novel DCNN architecture that can handle a variety of pedestrian appearances and urban surroundings by utilizing well-known datasets such as Penn-Fudan and PnPLO. Our model performs exceptionally well in terms of accuracy, speed, and durability, according to a thorough evaluation that compares it to recognized benchmarks. In addition to making important advances, our study has shown novel approaches to enhancing performance in challenging environments, such as the analysis of transfer learning and the deployment of real-time systems. This work paves the way for future developments in intelligent, self-governing systems and is a maj or leap forward in pedestrian detection technology.

关键词： Training Pedestrians Computational modeling Surveillance Transfer learning Urban planning computer architecture

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Optimizing Performance of Cloud Computing Management Algorithm for High-Traffic Networks 2

Optimizing Performance of Cloud Computing Management Algorit...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gorantla, Venkata Ashok K Sriramulugari, Shiva Kumar Gorantla, Bhargavi Yuvaraj, N. Singh, Kiran Solution Architect United States Sr. Lead Software Engineer United States Sri Shakthi Institute of Engineering and Technology Department of Cse Coimbatore India G.L. Bajaj Institute of Management Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Cloud computing has revolutionized the way people use technology, enabling a variety of virtual services and applications in the corporate world. The improved cost-efficiency and flexibility of cloud computing have made it an attractive option for companies that require high-traffic networks. However, this has also increased the need for optimizing network performance in order to ensure reliable service and a pleasing experience for users. To this end, various algorithms have been proposed for cloud computing management. However, these algorithms often conflict with each other, leading to a decrease in performance. Thus, there is a need for a unified approach that can take into account different methods in a holistic manner. The optimization algorithms developed for cloud computing management are targeted towards improving resource utilization, failure handling, and load balancing. By properly allocating and balancing resources, the overhead associated with these tasks is greatly reduced. Furthermore, the intelligent use of techniques such as fault tolerance, capacity planning, and quality of service guarantees ensures that the users' experience within the cloud is of optimal quality. Parametric results from research on Cloud Computing management algorithm performance showed that, when correctly implemented, the algorithms can provide significant improvements to the management and performance of high-traffic networks. Specifically, performance improvements up to 40% were observed in resource allocation speed and energy consumption. © 2024 IEEE.

关键词： Load Balancing Network Traffic Resource Allocation Scalability Traffic Management

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network 7

The New Frontier in Media: AI-Driven Content Creation for Ad...

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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Ramagundam, Shashishekhar Karne, Niharika Comcast Software Development and Engineering in Content Discovery and Ai United States Troy University Masters in Computer Science AL. Department of Computer Science Montgomery Campus United States Dt Amtrak Sr. Principal Systems Engineer in C and Rs Platform Services United States University of South Alabama Masters in Electrical Engineering Department of Electrical Engineering United States

ISBN: (纸本)9798331505530

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics. © 2024 IEEE.

关键词： Generative adversarial networks

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