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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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PrivShape: Extracting Shapes in Time Series under User-Level Local Differential Privacy 40

PrivShape: Extracting Shapes in Time Series under User-Level...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Mao, Yulian Ye, Qingqing Hu, Haibo Wang, Qi Huang, Kai Southern University of Science and Technology Department of Computer Science and Engineering China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology China School of Computer Science and Engineering Macau University of Science and Technology China

ISBN: (纸本)9798350317152

Time series have numerous applications in finance, healthcare, IoT, and smart city. In many of these applications, time series typically contain personal data, so privacy infringement may occur if they are released directly to the public. Recently, local differential privacy (LDP) has emerged as the state-of-the-art approach to protecting data privacy. However, existing works on LDP-based collections cannot preserve the shape of time series. A recent work, PatternLDP, attempts to address this problem, but it can only protect a finite group of elements in a time series due to ?-event level privacy guarantee. In this paper, we propose PrivShape, a trie-based mechanism under user-level LDP to protect all elements. PrivShape first transforms a time series to reduce its length, and then adopts trie-expansion and two-level refinement to improve utility. By extensive experiments on real-world datasets, we demonstrate that PrivShape outperforms PatternLDP when adapted for offline use, and can effectively extract frequent shapes. © 2024 IEEE.

关键词： Time series

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Lightweight Privacy Preserving Framework at Edge Layer in IoT 1

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Agrawal, Kavita Chittineni, Suresh Reddy, P. V. G. D. Prasad Subhadra, K. Diaz, Elizabeth D. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam India Department of Computer Science and Systems Engineering Andhra University Vishakhapatnam India Department of Computer Science and Engineering University of Texas Arlington United States

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

The Internet of Things is helping us to make our lives comfortable. IoT devices used here are resource-constrained in terms of memory and CPU as well as they lack basic security design. When everyone is using IoT, there are high chances of their personal data getting exposed to the outside world. For real-time application, data need to be processed at the edge layer directly instead of doing the entire processing and analysis at cloud. Privacy preserving at the edge layer in the Internet of Things is a challenging task. We proposed a lightweight privacy preserving framework that uses the concept of blockchain, zero knowledge proof algorithm, principal component analysis, and digital signatures. Here for the mining process in blockchain, Trust concept is being used instead of Proof of Work (POW) and Proof of Stake (POS) as it consumes fewer resources and our edge layer is resource constrained. This could be a feasible solution and can be deployed at the edge layer in the Internet of Things for preserving privacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

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computer systems science & engineering 2023年第8期46卷 1533-1547页

作者： Saud Yonbawi Sultan Alahmari T.Satyanarayana Murthy Padmakar Maddala E.Laxmi Lydia Seifedine Kadry Jungeun Kim 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 Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Civil Engineering Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop *** agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental *** control has become one of the significant problems in the agricultural *** traditional weed control,the entire field is treated uniformly by spraying the soil,a single herbicide dose,weed,and crops in the same *** more precise farming,robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed *** may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable *** study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection(HHOGCN-WD)technique for Precision *** HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision *** image pre-processing,the HHOGCN-WD model utilizes a bilateral normal filter(BNF)for noise *** addition,coupled convolutional neural network(CCNet)model is utilized to derive a set of feature *** detect and classify weed,the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection *** experimental results of the HHOGCN-WD technique are investigated under the benchmark *** results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches,with increased accuracy of 99.13%.

关键词： Weed detection precision agriculture graph convolutional network harris hawks optimizer hyperparameter tuning

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Trauma-Informed Insights from Co-Design of Self-Disclosure Robots with Domestic Abuse Survivors 20

Trauma-Informed Insights from Co-Design of Self-Disclosure R...

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20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Morris, Nyomi Carrine Williams, Tom Jelen, Ben Colorado School of Mines MIRRORLab GoldenCO United States Rose-Hulman Institute of Technology Department of Computer Science and Software Engineering Terre HauteIN United States

ISBN: (纸本)9798350378931

Survivors of domestic abuse face significant challenges securing recovery resources, such as housing, mental health care, and social connections. Accessing these resources requires survivors to disclose their status as survivors of domestic abuse;a process that can be traumatic and emotionally burdensome. In this work, we consider how social robots might help domestic abuse survivors to address challenges surrounding self-disclosure. To do so, we conducted a three-session co-design workshop involving 8 participants from a domestic abuse shelter. Our results provide three key contributions: (1) new insights into the benefits and barriers to self-disclosure;(2) new insights into the ways that social robots can help survivors to better achieve those benefits and overcome those barriers before, during, and after disclosure, including key design recommendations associated with each of these phases;and (3) observations into how a trauma-informed computing perspective may enable more effective design work in Human-Robot Interaction. © 2025 IEEE.

关键词： Social robots

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Transformative Technology in Poultry Management: 3D CNNs for Broiler Chicken Weight Prediction 1

Transformative Technology in Poultry Management: 3D CNNs for...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Anuprabha, K. Kaliappan, Vishnu Kumar Baniekal Hiremath, Gangadhar Kpr Institute of Engineering and Technology Department of Computer Science Engineering Tamilnadu Coimbatore641407 India Wasp Autonomous Systems Pvt Ltd Karnataka Ballari India

ISBN: (纸本)9798350372748

In global food production, broiler chickens hold significant importance, which are traditionally managed through manual labour for both meat and egg production. However, the poultry farming industry has increasingly turned to automation to address challenges associated with manual labour and enhance efficiency. This research introduces a novel methodology for estimating chicken weight utilizing top-view videos as input and leveraging 3D Convolutional Neural Networks (CNNs). Accurate weight assessment is a critical aspect of poultry farming, influencing feeding strategies and resource allocation. Traditional manual weighing methods are laborious and time-intensive, necessitating the exploration of automated alternatives. The methodology involves dataset collection, preprocessing, and employing 3D CNNs for effective feature extraction. 3D CNN architecture, adept at capturing spatial and temporal features, consists of convolutional and pooling layers, followed by flatten and dense layers for weight prediction. The model achieves a remarkable accuracy of 95%, as evidenced in the experimental results. Experimental validation showcases the potential of this method as a non-invasive and efficient tool for estimating chicken weight. It contributes to the growing field of automated livestock management in agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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Ship Platform Access Control Method Based on Zero Trust 7

Ship Platform Access Control Method Based on Zero Trust

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Wei, Renlong Li, Guobing Beijing University of Posts and Telecommunications Beijing China Beihang University School of Computer Science and Engineering Beijing China Cssc Systems Engineering Research Institute China

ISBN: (纸本)9798350370782

With the digital transformation of the shipbuilding industry, the interaction between ship platform systems and intelligent equipment has become closer, which has led to the increasingly blurred security boundaries of ship platform systems and increased the risk of illegal access to ship platform resources. At present, ship platforms still mainly rely on static access control methods to protect sensitive resources, which can no longer meet the growing access control needs in a de-bordered environment. In order to meet this challenge, this paper introduces the zero-trust concept into the ship platform access control system. By designing a trust evaluation algorithm and a dynamic access control algorithm, the ship platform permissions can be managed more finely, and experiments are designed to verify the effectiveness of the scheme in access control. © 2024 IEEE.

关键词： Shipyards

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Aspect-Based Sentiment Analysis on Amharic Text for Evaluating Ethio-Telecom Services 2

Aspect-Based Sentiment Analysis on Amharic Text for Evaluati...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Tesfa, Tarikwa Belete, Befikadu Abera, Samuel Mohapatra, Sudhir Kumar Das, Tapan Kumar Addis Ababa Science and Technology University Department of Software Engineering Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Sri Sri University Faculty of Emerging Technologies Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN. © 2024 IEEE.

关键词： Sentiment analysis

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A survey on deep learning for polyp segmentation: techniques, challenges and future trends

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Visual Intelligence 2025年第1期3卷 1-20页

作者： Mei, Jiaxin Zhou, Tao Huang, Kaiwen Zhang, Yizhe Zhou, Yi Wu, Ye Fu, Huazhu PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Institute of High Performance Computing A*STAR Singapore Singapore

Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had problems capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in the field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, and then describe benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp size, taking into account the focus of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in the field. © The Author(s) 2025.

关键词： Comprehensive evaluation Deep learning Medical imaging Polyp segmentation

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