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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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Smart Monitoring and Irrigation Regulation via IoT and Cloud 8

Smart Monitoring and Irrigation Regulation via IoT and Cloud

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sumalatha, Konatham Harshdeep, Kumar Vellore Institute of Technology School of Computer Science & Engineering Department of Database Systems Vellore India Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350376425

The increasing demand for agricultural produce and the strain on global water resources highlight the need for innovative solutions to improve water efficiency in farming. This work introduces an IoT-powered Smart Irrigation System designed to optimize water usage, ensuring sustainable crop cultivation. Traditional irrigation methods often lead to inefficiencies, wasting water and increasing costs, as they fail to consider varying soil moisture and environmental conditions across fields. This can result in suboptimal crop growth and resource depletion. To overcome these challenges, the proposed system integrates IoT technology with smart irrigation practices. Sensors deployed in the field collect real-time data on soil moisture, temperature, humidity, and light intensity, which a central controller then processes. Using cloud-based analytics, the controller adjusts irrigation schedules and water distribution to match current conditions precisely. The system's primary goal is to create an eco-friendly, cost-effective solution that reduces water waste, lowers operational costs, and minimizes environmental impact. Scalable and adaptable to various crops and environments, this smart irrigation system promises to improve crop yields, promote sustainable agriculture, and address water scarcity challenges globally. © 2024 IEEE.

关键词： Cloud Computing Real-Time Data Analytics Smart Irrigation Soil Moisture Sensors

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CounterFair: Group Counterfactuals for Bias Detection, Mitigation and Subgroup Identification 24

CounterFair: Group Counterfactuals for Bias Detection, Mitig...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Kuratomi, Alejandro Lee, Zed Tsaparas, Panayiotis Junior, Guilherme Dinis Pitoura, Evaggelia Lindgren, Tony Papapetrou, Panagiotis Stockholm University Department of Computer and Systems Sciences Sweden University of Ioannina and Archimedes/Athena RC Department of Computer Science and Engineering Greece

ISBN: (纸本)9798331506681

Counterfactual explanations can be used as a means to explain a models decision process and to provide recommendations to users on how to improve their current status. The difficulty to apply these counterfactual recommendations from the users perspective, also known as burden, may be used to assess the models algorithmic fairness and to provide fair recommendations among different sensitive feature groups. We propose a novel model-agnostic, mathematical programming-based, group counterfactual algorithm that can: (1) detect biases via group counterfactual burden, (2) produce fair recommendations among sensitive groups and (3) identify relevant subgroups of instances through shared counterfactuals. We analyze these capabilities from the perspective of recourse fairness, and empirically compare our proposed method with the state-of-the-art algorithms for group counterfactual generation in order to assess the bias identification and the capabilities in group counterfactual effectiveness and burden minimization. © 2024 IEEE.

关键词： Algorithmic Fairness Counterfactual explanations Group counterfactuals Local explainability

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Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

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IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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Detection of Artery/Vein in Retinal Images Using CNN and GCN for Diagnosis of Hypertensive Retinopathy 4th

Detection of Artery/Vein in Retinal Images Using CNN and GCN...

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4th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2023

作者： Al Sariera, Esra’a Mahmoud Jamil Padma, M.C. Al Sariera, Thamer Mitib Department of Computer Science and Engineering PES College of Engineering Mandya University of Mysore Mysore India Department of Computer Science and Information Systems Amman Arab University Amman Jordan

ISBN: (纸本)9789819965854

A progressive disease of the retina known as hypertensive retinopathy (HR) is linked to both high blood pressure and diabetes mellitus. The severity and persistence of high blood pressure are directly connected with the development of HR. The results of the HR include constricted arterioles, retinal hemorrhage, macular edema, and cotton-like patches as symptoms of eye pathological problems. In this paper, a novel strategy that combines convolutional neural network (CNN) and graph convolutional network (GCN) is proposed to improve the accuracy of categorizing retinal blood vessels and classification of HR phases. The process is extracting vessel features from the spatial domain and representing them using graphs. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Novel Optimized Feature Selection Using Metaheuristics Applied to Physical Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4027-4041页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Fadwa Alrowais Sunil Kumar Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt School of Computer Science University of Petroleum and Energy StudiesDehradun248001India Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

In data mining and machine learning,feature selection is a critical part of the process of selecting the optimal subset of features based on the target *** are 2n potential feature subsets for every n features in a dataset,making it difficult to pick the best set of features using standard ***,in this research,a new metaheuristics-based feature selection technique based on an adaptive squirrel search optimization algorithm(ASSOA)has been *** using metaheuristics to pick features,it is common for the selection of features to vary across runs,which can lead to *** of this,we used the adaptive squirrel search to balance exploration and exploitation duties more evenly in the optimization *** the selection of the best subset of features,we recommend using the binary ASSOA search strategy we developed *** to the suggested approach,the number of features picked is reduced while maximizing classification accuracy.A ten-feature dataset from the University of California,Irvine(UCI)repository was used to test the proposed method’s performance *** other state-of-the-art approaches,including binary grey wolf optimization(bGWO),binary hybrid grey wolf and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hybrid GWO and genetic algorithm 4028 CMC,2023,vol.74,no.2(bGWO-GA),binary firefly algorithm(bFA),and *** results confirm the superiority and effectiveness of the proposed algorithm for solving the problem of feature selection.

关键词： Metaheuristics adaptive squirrel search algorithm optimization methods binary optimizer

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