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ChatGPT-guided Semantics for Zero-shot Learning

ChatGPT-guided Semantics for Zero-shot Learning

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Shubho, Fahimul Hoque Chowdhury, Townim Faisal Cheraghian, Ali Saberi, Morteza Mohammed, Nabeel Rahman, Shafin North South University Dept. of Electrical and Computer Engineering Bangladesh University of Adelaide Australian Institute for Machine Learning Australia Data61 Commonwealth Scientific and Industrial Research Organisation Australia University of Technology Sydney School of Computer Science and Dsi Australia

ISBN: (纸本)9798350382204

Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language models (like word2vec). We know attribute annotation is costly, whereas automatic word vectors are relatively noisy. To address this problem, we explore how ChatGPT, a large language model, can enhance class semantics for ZSL tasks. ChatGPT can be a helpful source to obtain text descriptions for each class containing related attributes and semantics. We use the word2vec model to get a word vector using the texts from ChatGPT. Then, we enrich word vectors by combining the word embeddings from class names and descriptions generated by ChatGPT. More specifically, we leverage ChatGPT to provide extra supervision for the class description, eventually benefiting ZSL models. We evaluate our approach on various 2D image (CUB and AwA) and 3D point cloud (ModelNet10, ModelNet40, and ScanObjectNN) datasets and show that it improves ZSL performance. Our work contributes to the ZSL literature by applying ChatGPT for class semantics enhancement and proposing a novel word vector fusion method. © 2023 IEEE.

关键词： Zero-shot learning

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Forensic science: AI-Powered Image and Audio Analysis

Forensic Science: AI-Powered Image and Audio Analysis

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： P Renukadevi Sincy John N Shivani Dept. of Computer Science and Engineering Jain University Bangalore India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Forensic science demands the precise application of advanced scientific principles and procedures to investigate crimes and ensure justice. This process involves the analysis of vast and complex data. The research explores the potential of modern science and technologies, particularly Artificial Intelligence (AI), to develop innovative techniques across various branches of forensic science. It provides a comprehensive examination of AI applications in forensic fields, focusing on image and audio analysis to identify instruments used in crimes and determine causes of death. By integrating AI, the research aims to enhance the accuracy and efficiency of forensic investigations while maintaining ethical standards. The paper also discusses the difficulties with AI in forensic science, highlighting the need for open and moral AI systems. It highlights that forensic errors leading to wrongful convictions often result from factors such as incompetence, spam, weak scientific foundations, or organizational deficiencies, rather than blunders made by forensic scientists. These issues highlight the importance of addressing the root causes of forensic errors to improve the reliability of forensic practices. The study also delves into the specific challenges AI encounters, such as data quality, interpretability of AI models, and the unification of AI into current forensic procedures. By tackling these challenges, the research aims to ensure that AI applications in forensic science are effective and trustworthy. Overall, this work highlights the critical role of AI in advancing forensic science and the importance of addressing the underlying issues that contribute to forensic errors, ultimately striving for a more reliable and just forensic process

关键词： Ethics Accuracy Forensics Instruments data integrity Focusing data models Reliability Artificial intelligence Standards

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HALDA: An Adaptive IoT Algorithm for Reducing Water Losses in Jordanian Homes

HALDA: An Adaptive IoT Algorithm for Reducing Water Losses i...

引用

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Abu Arqoub Eman Alduweib Waseem Alromema Ali A. Titinchi dept. of Computer Science University of Petra Amman Jordan dept. of Computer Science and Information Taibah University Medinah Saudi Arabia dept. of Electrical and Computer Engineering University of Nizwa Nizwa Sultanate of Oman

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Water is considered an extremely valuable commod-ity in Jordan, which happens to be among those countries of the world that have the lowest water availability per capita. This paper introduces the Hybrid Adaptive Leak Detection Algorithm (HALDA), an IoT-based framework adapted for resi-dential water management in resource-constrained environments. HALDA integrates real-time sensing, adaptive thresholding, and predictive analytics into leakage detection and mitigation at the household level. The system, by continuously sending notifications related to data-driven analysis, helps lower not only acute water wastage events, but also encourages better pro-sustainable habits among home holders. The accuracy results for the experiments in detecting accurate detection and power draw from ten conducted field trials all over Amman was as highly as 93 %, with each sensor as well as being that low at each 50 mW, reducing widely previously undetected leaks. Results stress the fact that HALDA is one of the few promising approaches toward finding a cost-effective and energy-efficient solution to the water scarcity problem of Jordan and many other arid and semi-arid regions of the world.

关键词： Accuracy data integrity Urban areas Water conservation Predictive models Real-time systems Blockchains Leak detection Predictive analytics Water resources

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County-Level Associations Between Social and Sleep Deprivation Conditioned by Regional Effects

County-Level Associations Between Social and Sleep Deprivati...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Swapna S. Gokhale Dept. of Computer Science & Engineering University of Connecticut Storrs CT

ISBN: (数字)9798350383737

ISBN: (纸本)9798350383744

Insufficient sleep leads to numerous negative health out-comes, and yet, approximately one-third of the adult U.S. population receives less than the recommended amount of sleep for optimal health. Consequently, sleep deprivation is now recognized as a public health crisis. Strategies to encourage optimal sleep must consider the social, environmental’ and cultural factors linked to insufficient sleep, although studies exploring these links are limited. This paper utilizes machine learning to address a gap in epidemiological modeling by conducting a joint county-level analysis of PLACES data and social deprivation measures across four U.S. regions. The innovation of this research stems from the use of random forest models, which include spatial autocorrelation structures within the modeling framework, commonly applied in ecology and earth science. Our findings reveal that incorporating spatial structures enhances the models' explainability by approximately 20%. Social deprivation measures can account for roughly 70% of the prevalence of sleep deficiency in the South and Midwest, but only about 55% in the Northeast and West, indicating that additional factors, such as the cultural glorification of sleep deprivation and urban lifestyles with lengthy commutes, may influence these regions. An analysis combining all four regions indi-cates that unemployment and educational attainment, rather than poverty as previously suggested, are the primary social deprivation factors affecting sleep duration. These insights could inform intervention strategies to promote optimal sleep, aligning with the objectives of Healthy People 2030.

关键词： Technological innovation Biological system modeling Sociology Length measurement Cultural differences Unemployment Statistics

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Blockchain Technology for Enhancing Trust and Security in Mobile Networks 2

Blockchain Technology for Enhancing Trust and Security in Mo...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E. Pawar, Priyanka University Of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States

ISBN: (纸本)9798350365269

Blockchain technology, the backbone of popular cryptocurrencies like Bitcoin, has garnered massive interest in recent years for its capability to revolutionize traditional economic structures. However, Blockchain applications extend beyond simple finance and hold promising implications for mobile enterprises. One primary mission in cellular networks is maintaining accuracy and protection among customers and service providers. Traditional methods of securing records, including centralized databases, are susceptible to cyber assaults and data breaches. Cellular network security can be improved by using the decentralized, immutable structure that Blockchain technology provides. By leveraging Blockchain, cellular networks can utilize the core features of the distributed ledger, consensus, and encryption to create a more secure environment. The distributed ledger ensures that transactions among customers and service providers are recorded and verified using a network that doesn't rely on a central authority. The consensus mechanism ensures that any change or update to the network is agreed upon by the majority, making it exceedingly difficult for hackers to manipulate the system. © 2024 IEEE.

关键词： Blockchain

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Dynamic Task Scheduling for Load Balancing in Cloud Environments to Enhance Resource Allocation and Performance Efficiency

Dynamic Task Scheduling for Load Balancing in Cloud Environm...

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Innovations and Challenges in Emerging Technologies (ICICET), International Conference on

作者： Mohammad Imran Khan Kapil Sharma Dept. of Computer Science and Engineering Amity University Gwalior India

ISBN: (数字)9798350319019

ISBN: (纸本)9798350319026

Effective resource distribution and speed efficiency are key to giving people the best service in cloud computer settings. Dynamic task ordering is a key part of reaching these goals because it uses available resources to easily distribute jobs while balancing the load. Within this paper, we suggest a new way to handle dynamic task scheduling that will help make cloud settings better at allocating resources and optimizing speed. The suggested approach uses a dynamic scheduling program that considers things like available resources, job needs, and present system load to make smart schedule choices. With dynamic task assignment, the system can change to changing job conditions in real time, making better use of resources and reducing response times. The suggested method includes load balance, which spreads jobs fairly among available resources so that computer or server doesn’t get too busy. A fair job division keeps the system running smoothly, even during times of high usage. To make the best use of resources and reduce energy use, the suggested method also includes techniques like virtual machine consolidation and transfer. There is better resource usage and energy efficiency in the system because tasks are centralized on fewer actual computers and resources are allocated based on job demand. Based on results from experiments, the suggested method is better at allocating resources and improving performance compared to standard scheduling methods. In terms of shorter reaction times, higher output, and better use of resources, the results show big gains.

关键词： Cloud computing Schedules Technological innovation Recurrent neural networks Processor scheduling Companies Dynamic scheduling

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Hindering Search and Rescue Missions with Selective Wireless Jamming Attacks

Hindering Search and Rescue Missions with Selective Wireless...

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IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Javad Mokhtari Koushyar Mina Guirguis George Atia Dept. of Computer Science Texas State University Dept. of Electrical and Computer Engineering University of Central Florida

ISBN: (数字)9798350362817

ISBN: (纸本)9798350362824

In search and rescue missions, agents are tasked with locating targets in dynamic and potentially adversarial environments. These agents commonly operate under energy constraints and rely on wireless communication susceptible to intentional interference by attackers. Thus, it is important to study the impact of attackers deploying stealthy wireless jamming attacks to disrupt such missions. To this end, this paper introduces a Markov Decision Process (MDP) model designed for the attacker, determining when to jam communication based on the evolving state of the mission. A reinforcement learning-based approach is developed to identify intelligent and selective jamming policies, particularly in scenarios where the identification of optimal strategies is computationally prohibitive due to the expansive state space. These policies judiciously optimize the tradeoff between the cost of the attack and the expected damage inflicted. We evaluate the performance of these policies in environments of varying sizes. We demonstrate that our approach achieves optimal performance in tractable setups and outperforms alternative heuristics as the complexity of the state space increases.

关键词： Wireless communication Learning systems Costs Markov decision processes Switches Interference Timing

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Novel Plant Leaf Disease Detection Approach using Hybrid Deep Learning Strategy 5

Novel Plant Leaf Disease Detection Approach using Hybrid Dee...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Vamshi, Jalla Tanwar, Shashi Thatipudi, Jayendra Gopal Ponsangeetha, A. Mukherjee, Saptarshi Mandhare, Jagdish B. School of Agriculture Sr University Telangana Warangal India G D Goenka University Department- Computer Science and Engineering Haryana Gurugram India University of North Texas Dept. of Computer Science United States Velammal Institute of Technology Department of Computer Science and Engineering Panchetti India Goldsmith Institute of Advanced Research Department-Mtech Bioinformatics Giar West Bengal Kolkata India Bharati Vidyapeeth College of Engineering Department of Instrumentation Navi Mumbai India

ISBN: (纸本)9798350385649

The early detection and identification of plant diseases are pivotal for precision agriculture, aiming to mitigate crop losses and optimize yields. This study presents a novel approach to plant disease identification, leveraging Deep Learning (DL) techniques with a focus on transfer learning. Traditional methods often rely on complex image enhancement, disease region segmentation, and feature extraction, whereas our approach employs Convolutional Neural Networks (CNNs) for efficient and accurate disease detection. To enhance accessibility and diagnostic capabilities, we implement Hybrid DL Strategy, which consists of EfficientNetB0 and MobileNetV2 model, tailored for lightweight applications suitable for smartphone implementation. Efficient NetB0 model, which considers dept., width, and resolution during convolution, enhancing the model's capacity to capture intricate features critical for accurate disease diagnosis. The overall accuracy of proposed method is 98.44%, surpassing CNN, ResNet, and AlexNet. This high accuracy underscores the effectiveness of the proposed model in correctly classifying both diseased and healthy plant leaves. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases

A Novel Hybrid Deep Neural Network for Early Detection and C...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Shihab Uddin Md Fakrul Islam Sharukh Rahman Nasir Uddin Foysal Muhammad Nazrul Islam Md. Abdur Razzaque Faiz Al Faisal Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases.

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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Next-Generation Computing, IoT and Machine Learning (NCIM), International Conference on

作者： Md Fozle Rabbi Sraboni Das Dola Noorjahan Binte Kader Taha Tamim Al Mahmud Muhammad Nazrul Islam Md. Abdur Razzaque Faiz Al Faisal Dept. of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering University of Dhaka Dhaka Bangladesh

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed “FlowerConvNet”. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy.

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