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

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

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IEEE Conference on Vehicular Technology (VTC)

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

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

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.

关键词： Crowdsourcing 6G mobile communication Wireless communication Vehicular and wireless technologies Authentication Resists Blockchains Security Drones Resilience

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Social Media Multi-Emotion Detection

Social Media Multi-Emotion Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Soha S. Ibrahiem Bahy Ibrahim El-Sayed Mohammed Mohammed Ahmed El Sayed Ahmed Khaled ElBahnasy Computality R&D System Analyst & NLP Researcher Faculty of science Menofia university Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Computer Science and Information Technology Department Jeddah international College Jeddah KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

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.

关键词： Support vector machines Technological innovation Accuracy Social networking (online) Smart cities Profitability Semantics Lexicon Feature extraction Vectors

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Harnessing the Synergy Between Artificial Intelligence and Blockchain for Enhancing Transparency Security

Harnessing the Synergy Between Artificial Intelligence and B...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Monika Anand Sudharshan Putha Ismail Keshta Swaroop Reddy Gayam Azzah AlGhamdi Department of CSE Chandigarh University Mohali Software Developer at Salesforce USA Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Software Engineer at TJMax USA College of CSIT Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

In the recommended technique, AI and blockchain technology improve system stability and projected maintenance. This innovative technique begins with sensor data collection and preprocessing. This covers cleaning, normalizing, and extracting characteristics for AI model training. The model’s projections set alarming limitations. These are securely stored on a blockchain for data transparency. Continuous feedback loops make data processing safer and enhance model prediction. Combining monitor data with model projections to identify problems and provide effective solutions makes the system more trustworthy. Adaptive beamforming improves optical communication by changing beamforming weights and channel responses in real time, enhancing signal integrity and performance. The technique enhances transparency, security, automation efficiency, scalability, portability, data integrity, privacy protection, system speed, resource usage, fault tolerance, cost-effectiveness, and user experience. Simple, safe, and effective system management and predictive maintenance are demonstrated by their consistently higher performance than prior methods. AI and blockchain work together to establish a new standard by eliminating current constraints and changing all elements.

关键词： Data privacy Adaptation models Automation Scalability User experience Blockchains Security Artificial intelligence Protection Predictive maintenance

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Enhancing Viewer Engagement: Exploring Generative Long Short Term Memory in Dynamic Ad Customization for Streaming Media

Enhancing Viewer Engagement: Exploring Generative Long Short...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Shashishekhar Ramagundam Niharika Karne Department of computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Troy University Montgomery Campus AL. Department of electrical engineering Masters in electrical engineering Sr. Principal Systems Engineer in C & RS Platform services DT Amtrak USA University of south alabama

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

In Ad customization, several approaches are employed to improve the relevance level of the message for monitoring the user performance according to the prior search history (e.g., retargeting) or by gathering particular information related to the customer (e.g., micro-targeting). Occasionally, the consumers view the ad developed by the other user, in this situation, the ads are viewed as something that can be related by the user. The quantity of information that is gathered openly or secretly can affect the user choice for the content displayed. However, the users remain as recipients of a strategy, that decides the best ad content. Therefore, an exploring Generative Artificial Intelligence-based Dynamic Ad Customization model for enhancing viewer engagement from streaming media is developed. In this paper, the Generative Long Short-Term Memory (GenLSTM) model is developed for examining customized advertising then includes the customization of specific message elements by individuals, variation in the level of ad customization, and in some cases perceived collaboration with others. Here, the customization classification is defined based on the modification in the marketing mix element that is created by the customer proactively as opposed to the company. In regards to customized advertising, “ad is something that happens with the viewer”. The customization increases based on the sense of task engagement, strength of the perceived customer relationship with the company, attitude toward the ad, and perceptions of company trust/integrity. Finally, the experimental analysis is conducted based on the relationship with the company, company care, company trust, and task engagement to attain the overall impact on ad customization.

关键词： Supply chains Collaboration Companies Streaming media Data models History Advertising Long short term memory Monitoring Consumer electronics

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An improved AI-driven Data Analytics model for Modern Healthcare Environment

An improved AI-driven Data Analytics model for Modern Health...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Prashamsh Takkalapally Balaram Yadav Kasula J. Logeshwaran Senior Software Engineer USA Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

AI-driven statistics analytics is a swiftly advancing and impactful era that is transforming the face of healthcare. By leveraging the energy of AI computing and gadget studying, healthcare organizations can speedy gain insights from their huge datasets, offering a greater comprehensive and personalized approach to hospital therapy and populace health management. This paper explores the advantages of AI-driven statistics analytics in healthcare settings, masking key benefits along with progressed analysis and treatment, better-affected person effects, and financial savings. Moreover, this paper addresses the main challenges associated with AI-pushed analytics and offers potential solutions to enhance accuracy and relevance. In the long run, statistics analytics powered by way of AI gives powerful opportunities to improve healthcare outcomes, and its use is expected to expand within the coming years.

关键词： Accuracy Hospitals Face recognition Medical services Organizations Manuals Real-time systems Data models Artificial intelligence Information analysis

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Examining the Benefits of AI in Wearable Sensor-based Healthcare Solutions

Examining the Benefits of AI in Wearable Sensor-based Health...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Prashamsh Takkalapally Balaram Yadav Kasula Sachin C. Patil J. Logeshwaran Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Senior Software Engineer USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The emergence of the AI generation has introduced adjustments to the manner healthcare solutions are advanced and applied. Wearable sensor-based total healthcare answers were revolutionized by leveraging AI in diverse packages. AI may be used to enhance the accuracy and precision of facts analysis, simplify information collection procedures, and pick out affected person-unique styles from the accrued data. Furthermore, AI can provide both actual-time and predictive analytics abilities, which are particularly useful for devising personalized healthcare offerings. Its provision of scalable systems hurries up the traits of various programs and has enabled personalized healthcare answers to be deployed in a shorter length. In spite of a number of the associated challenges, including data privacy troubles, AI-based wearable sensor-based healthcare answers can revolutionize patient tracking and timely detection of capability health conditions, improve preventive fitness care, and decrease healthcare fees.

关键词： Industries Data privacy Accuracy Medical services Artificial intelligence Prognostics and health management Predictive analytics

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DeepJDOT: Deep joint distribution optimal transport in facial recognition 2

DeepJDOT: Deep joint distribution optimal transport in facia...

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2nd International Conference on Mechatronics and Smart Systems, CONF-MSS 2024

作者： Cai, Yuchen Wen, Chenglong Chen, Haoyuan Liu, Yijia Peng, Haofei Computer Science and Software Engineer Department Rose-Hulman Institute of Technology Terre Haute47803 United States Department of Physics University of Toronto TorontoM5S 1A1 Canada Department of Physics University of Washington Seattle98195 United States Department of Water Conservancy and Civil Engineering China Agricultural University Beijing100083 China Department of Information Engineering Xi'An Jiaotong University Xi'an710000 China

This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation (DeepJDOT) method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face recognition. Furthermore, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Enhancing Trust in Wireless Ad-Hoc Network Applications Using Blockchain Technology 15

Enhancing Trust in Wireless Ad-Hoc Network Applications Usin...

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

作者： Rongali, Aruna Sri Saxena, Shweta Salma, Umi Alkhammash, Manal Mishra, Isa Aich, Shibani C Ph.D University of The Cumberlands Sr. Software Engineer Information Technology Emphasis/Information Technology United States University amity business school Amity university madhya pradesh Madhya pradesh gwalior India Jazan University Department of Computer Science Jazan Saudi Arabia KIIT Deemed to be University School of Management College KIIT School of Management Bhubaneswar India Amrita Vishwa Vidyapeetham Department of English Amritapuri India

ISBN: (纸本)9798350370249

This look investigates the utility of blockchain technology for reinforcing consideration in Wi-Fi networks. Networks are verbal exchange networks shaped on-the-fly, requiring no predefined infrastructure for dependable verbal exchange of facts. With the boom in IoT gadgets, wireless sensors, linked buses, and drones, the quantity of networks is anticipated to increase. The security and trustworthiness of networks rely on the trustworthiness of the nodes within the network, and this turns into a chief difficulty in ad-hoc networks. The proposed approach looks at the usage of the benefits of the blockchain era along with disbursed garages, clever contracts, and cryptography to enhance consideration in networks. The blockchain generation can validate the beginning and integrity of facts and assist an agreed/popular device, which would provide a straightforward manner for the nodes in an ad-hoc network to believe every difference. Wi-Fi ad-hoc networks are prone to malicious attacks because of the decentralized and allotted environment.. © 2024 IEEE.

关键词： Ad hoc networks

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An Investigation Method Incorporates with Contextual Information and Transformer Residual BiLSTM to Improve the Accuracy of Detecting Sensitive Content in Various Contexts

An Investigation Method Incorporates with Contextual Informa...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Niharika Karne Chandra Shekar Ramagundam Ranga Rao Patnala Shashishekhar Ramagundam Sai Nitisha Tadiboina Department of Electrical Engineering Masters in Electrical Engineering Sr. Principal Systems Engineer in C&RS Platform Services DT Amtrak University of South Alabama USA Senior Software Engineer in Cloud Platform Services City Of Philadelphia Pennsylvania USA Vice President US AL. Department of Computer Science Masters In Computer Science Software Development & Engineering in Content Discovery & AI Comcast Troy University Montgomery Campus USA Department of Information Technology GEICO - Chevy Chase MD United States

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

In the modern era, the internet is an emerging technology with a rapidly growing user. Sensitive content uploaders consider this platform as a way to transmit disturbing visual content, such as animated cartoon videos to circulate unsuitable content among youngsters. Previous researches show that the interaction of online sensitive information has substantial offline effects on historically impoverished communities. As a result, it is strongly recommended that social media platforms have an automatic real-time sensitive content filtering method for detecting contextual information. In this proposed model, a novel deep-learning framework is developed for detecting sensitive content from contextual information. For this, the suggested architecture performs under three stages (i) text collection, (ii) preprocessing, and (iii) detection. Initially, the required texts from the contextual information are gathered from the standard available data resource. The collected text is subjected to text preprocessing, where the text is cleaned and prepared for further analysis. Finally, in the detection phase, the developed Transformer Residual Bidirectional Long Short-Term Memory (TR-BiLSTM) is employed to learn the effective representation from the preprocessed text and perform the sensitive content detection. Furthermore, performance comparisons are carried out between the proposed frameworks with other standard approaches. The developed architecture achieved better results in detecting the sensitive content from the text.

关键词： Visualization Accuracy Recurrent neural networks Noise Transformers Real-time systems Long short term memory Standards Context modeling Videos

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A Random Forest Stock Prediction Model Based on Bayesian Optimization

A Random Forest Stock Prediction Model Based on Bayesian Opt...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yajuan Zhang Xiuyan Zheng Sihan Yang Shangyu Meng Z. Y. Yang Xinghui Fei College of Information Engineering Hainan Vocational University of Science and Technology Haikou Hainan China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Network engineer department Guiyang Shengteng Network Technology Co. LTD Guiyang China Electrical and Computer Engineer Deparment Colorado State University Fort Collins U.S.

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Random forest models often rely on human-determined parameters, which may introduce subjectivity and affect the degree of optimization of the model. To address this issue, we use a random forest approach based on Bayesian optimization to reduce the influence of human factors and improve the fitting. Bayesian optimization prevents the model from falling into the dilemma of local optimum. In order to show the effect of the optimization, we compare the support vector machine, the original random forest and the LGBM model, and perform Bayesian optimization on the latter two to achieve better results. Experiments show that the optimized model performs well in stock return prediction with a reduction of 0.01 in RMSE and MAE, a reduction of 0.4 in MAPE, and an improvement of 0.02 in R2 value compared to the other models. The Bayesian optimization based Random Forest model achieves the lowest prediction error on different datasets and has wide applicability.

关键词： Support vector machines Accuracy Time series analysis Human factors Predictive models Data models Bayes methods

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