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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Najmath Ottakath Somaya Al-maadeed Ahmed Bouridane Muhammad E.H Chowdhury Kishor Kumar Sadasivuni Department of Computer Science and Engineering Qatar University Doha Qatar Center of Data Analytics and Cybersecurity University of Sharjah Sharjah UAE Department of Electrical Engineering Qatar University Doha Qatar Center for Advanced Materials Qatar University Doha Qatar

Advancements in wearable electronics and portable sensors have enabled long-term and real-time health monitoring. Among these advancements, wearable ultrasound devices have garnered attention due to their portability, ease of use, and ability to penetrate deep tissues in the human body. The images generated by these devices can be harnessed for computer vision tasks, providing valuable insights into an individual's health status even during physical activity. This paper explores the design of such wearable ultrasound imagers, surveys their current applications, and offers insights into future prospects, including visual image processing and real-time analysis. The study also addresses the challenges of using wearable ultrasound for long-term health monitoring, emphasizing the need for ongoing innovation and research.

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A Design of an Effective Methodology to Detect Glaucoma Disease using Intelligent Learning Strategy 9

A Design of an Effective Methodology to Detect Glaucoma Dise...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Shobana, D. Akilarasu, G. Sangeetha, J. Priya, V. Banu Karkuzhali, S. Malathi, P. Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Lovely Professional University Department of Computer Science and Engineering Punjab India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Simats Department of Data Analytics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Globally, glaucoma is now among the leading causes of blindness. It is a chronic, irreversible illness. Screening for glaucoma and detecting it early are crucial in the fight against this illness. Here, deep learning shows great promise. Two deep learning architectures, the Convolutional Neural Network (CNN) model and the Improved Learning Strategy to Predict Glaucoma (ILSPG) model, have been evaluated in this article. In order to diagnose glaucoma, the DCNN method is utilized. Ultimately, this area of study aims to help doctors diagnose glaucoma by developing methods to spot the disease in retinal fundus pictures. Because glaucoma can have both beneficial and bad effects on the model, the result of the model might be good or bad. Several measures are utilized in the process of evaluating models, including accuracy, precision, specificity, and reliability. In order to determine the accuracy of the proposed model, an additional comparison study is conducted. The results are contrasted with a deep learning-based classification system that uses convolutional neural networks. The results demonstrate, without a doubt, that the proposed system is effective. © 2023 IEEE.

关键词： Convolutional neural networks

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Human-computer Interaction in Augmented Reality: Applications and Challenges

Human-Computer Interaction in Augmented Reality: Application...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International Engineering Conference on

作者： Eman Ahmad Shudayfat Raed Alazaidah Mohammad Aljaidi Marina Yusoff Maram Y. Al-Safarini Omar Alidmat Department of Creative media Luminus Technical University Amman Jordan Department of Data Science and AI Zarqa University Zarqa Jordan Departmemt of Computer Science Zarqa University Zarqa Jordan Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA (UiTM) Shah Alam-Selangor Malaysia Department of Computer Science Zarqa University Zarqa Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

a technique called Augmented Reality (AR) superimposes digital data on the physical world. This technology has the power to completely change how humans communicate with computers. Nonetheless, a number of issues still need to be resolved before AR user interfaces can be made to feel intuitive, natural, and effective. The applications and prospects of Human-computer Interaction (HCI) in AR are covered in this paper. Compared to conventional user interfaces, the authors contend that AR has the potential to produce more engaging and natural user interfaces. They also make the point that in order to fully grasp this potential, AR designers must create new interaction strategies and design tenets.

关键词： Human computer interaction computers Electric potential Artificial intelligence Augmented reality

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Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference

Galaxy: A Resource-Efficient Collaborative Edge AI System fo...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Shengyuan Ye Jiangsu Du Liekang Zeng Wenzhong Ou Xiaowen Chu Yutong Lu Xu Chen School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China IoT Thrust and Research Center for Digital World With Intelligent Things HKUST (Guangzhou) Guangzhou China Data Science and Analytics Thrust HKUST (Guangzhou) Guangzhou China Department of Computer Science and Engineering HKUST Hong Kong SAR China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload the inference workloads to the remote cloud server, which would induce substantial pressure on the backbone network as well as raise users’ privacy concerns. To address that, in-situ inference has been recently recognized for edge intelligence, but it still confronts significant challenges stemming from the conflict between intensive workloads and limited on-device computing resources. In this paper, we leverage our observation that many edge environments usually comprise a rich set of accompanying trusted edge devices with idle resources and propose Galaxy, a collaborative edge AI system that breaks the resource walls across heterogeneous edge devices for efficient Transformer inference acceleration. Galaxy introduces a novel hybrid model parallelism to orchestrate collaborative inference, along with a heterogeneity-aware parallelism planning for fully exploiting the resource potential. Furthermore, Galaxy devises a tile-based fine-grained overlapping of communication and computation to mitigate the impact of tensor synchronizations on inference latency under bandwidth-constrained edge environments. Extensive evaluation based on prototype implementation demonstrates that Galaxy remarkably outperforms state-of-the-art approaches under various edge environment setups, achieving up to 2.5× end-to-end latency reduction.

关键词： Tensors Computational modeling Collaboration Prototypes Edge AI Smart homes Parallel processing

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Dermatological Diagnostics: A Unified Deep Learning Framework for Skin Lesion and Cancer Classification

Dermatological Diagnostics: A Unified Deep Learning Framewor...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Kumar Isha Jain Durga Sritha Dongla Shivangi Rai Vani Rastogi Eva Kaushik Rohit Kaushik School of Engineering & Applied Science The George Washington University Washington D.C USA Computer Science & Engineering Department Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Computer Science & Engineering Department Gandhi Institute of Technology and Management Visakhapatnam Andhra Pradesh Data Scientist DXC Technology Noida India CS and Mathematics Department Data Analytics Program University of Illinois Springfield USA

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Skin pathologies encompass a spectrum of conditions, with malignancies such as melanoma representing a critical diagnostic urgency. This investigation delineates the deployment of Convolutional Neural Networks (CNNs) for the classification of dermatological anomalies, benchmarking CNN diagnostic fidelity against dermatological expert evaluations. The study underscores the efficacy of CNN classifiers in expediting the diagnostic workflow for various cutaneous disorders. Advocating for an automated diagnostic framework, the research introduces a CNN-based system aimed at reducing human diagnostic load, accelerating diagnostic timelines, and enhancing survival outcomes. Utilizing advanced image processing algorithms and deep learning architectures, the research presents an automated classification system for skin pathologies, addressing both benign and malignant presentations. The classification matrix includes nine dermatological conditions: actinic keratosis, basal cell carcinoma, benign keratosis, dermatofibroma, melanoma, nevus, seborrheic keratosis, squamous cell carcinoma, and vascular lesions. The objective is to engineer a CNN model with robust diagnostic performance across a diverse lesion dataset, ensuring accurate identification and categorization of dermatological conditions.

关键词： Deep learning Pathology Accuracy Melanoma computer architecture Benchmark testing Skin Convolutional neural networks Lesions Medical diagnosis

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Characterizing Zagreb Index Bounds in Trees with Specified Metric Dimension

arXiv

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arXiv 2024年

作者： Ali, Waqar Husin, Mohamad Nazri Bin Nadeem, Muhammad Faisal Special Interest Group on Modeling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan

Consider a simple graph G = (V, E), where V are the vertices and E are the edges. The first Zagreb index, M1(G) = Pv∈V ψG(v)2. The second Zagreb index, M2(G) = Puv∈E ψG(u)ψG(v). The metric dimension of a graph refers to the smallest subset of vertices in a resolving set such that the distances from these vertices to all others in the graph uniquely identify each vertex. In this paper, we characterize bounds for the Zagreb indices of trees, based on the order of the tree and its metric dimension. Furthermore, we identify the trees that achieve these extremal bounds, offering valuable insights into how the metric dimension influences the behavior of the Zagreb indices in tree structures. © 2024, CC BY.

关键词： Trees (mathematics)

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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Extremal Values of the Atom-Bond Connectivity Index for Trees with Given Roman Domination Numbers

arXiv

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arXiv 2024年

Consider that G = (X, Y) is a simple, connected graph with X as the vertex set and Y as the edge set. The atom-bond connectivity (ABC) index is a novel topological index that Estrada introduced in Estrada et al. (1998). It is defined as ABC(G) = xy∈XY (G)s ζx +ζxζζyy− 2 where ζx and ζx represent the degrees of the vertices x and y, respectively. In this work, we explore the behavior of the ABC index for tree graphs. We establish both lower and upper bounds for the ABC index, expressed in terms of the graph’s order and its Roman domination number. Additionally, we characterize the tree structures that correspond to these extremal values, offering a deeper understanding of how the Roman domination number (RDN) influences the ABC index in tree graphs. © 2024, CC BY.

关键词： Trees (mathematics)

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Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats

arXiv

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arXiv 2024年

作者： Pavlich, Ryan Ebadi, Nima Tarbell, Richard Linares, Billy Tan, Adrian Humphreys, Rachael Das, Jayanta Kumar Ghandiparsi, Rambod Haley, Hannah George, Jerris Slavin, Rocky Choo, Kim-Kwang Raymond Dietrich, Glenn Rios, Anthony Data Analytics The University of Texas San Antonio United States Department of Electrical and Computer Engineering The University of Texas San Antonio United States Department of Computer Science The University of Texas San Antonio United States Department of Information Systems and Cyber Security The University of Texas San Antonio United States

Recognizing the promise of natural language interfaces to databases, prior studies have emphasized the development of text-to-SQL systems. While substantial progress has been made in this field, existing research has concentrated on generating SQL statements from text queries. The broader challenge, however, lies in inferring new information about the returned data. Our research makes two major contributions to address this gap. First, we introduce a novel Internet-of-Things (IoT) text-to-SQL dataset comprising 10,985 text-SQL pairs and 239,398 rows of network traffic activity. The dataset contains additional query types limited in prior text-to-SQL datasets, notably temporal-related queries. Our dataset is sourced from a smart building's IoT ecosystem exploring sensor read and network traffic data. Second, our dataset allows two-stage processing, where the returned data (network traffic) from a generated SQL can be categorized as malicious or not. Our results show that joint training to query and infer information about the data can improve overall text-to-SQL performance, nearly matching substantially larger models. We also show that current large language models (e.g., GPT3.5) struggle to infer new information about returned data, thus our dataset provides a novel test bed for integrating complex domain-specific reasoning into LLMs. © 2024, CC BY.

关键词： Internet of things

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The application and comparison of Deep Learning models for the prediction of chest cancer prognosis

The application and comparison of Deep Learning models for t...

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International Conference on Smart Communications and Networking (SmartNets)

作者： Mahadi Hasan Miraz Al Mamun Madhab Chandra Das Md Maruf Hasan Asm Mohaimenul Islam MS in Data Analytics The University of Tennessee at Chattanooga Chattanooga Tennessee United States Health Outcomes & Analytics CHRISTUS Health Irving Texas USA Electrical and Electronic Engineering University of Information Technology and Sciences Dhaka Bangladesh Department of Computer Science University of South Dakota Vermillion SD USA Department of Computer Science University of South Dakota South Dakota USA

Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the ***, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures— CNN, ResNet50, and DenseNet121—. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study’s findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNet1

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