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Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

computers, Materials & Continua 2025年第5期83卷 1973-1986页

作者： Khulud Salem Alshudukhi Mamoona Humayun Ghadah Naif Alwakid Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computing School of Arts Humanities and Social SciencesUniversity of RoehamptonLondonSW155PHUK

The Internet ofThings(IoT)and edge computing have substantially contributed to the development and growth of smart *** handled time-constrained services and mobile devices to capture the observing environment for surveillance *** systems are composed of wireless cameras,digital devices,and tiny sensors to facilitate the operations of crucial healthcare ***,many interactive applications have been proposed,including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT ***,most solutions lack optimizing relayingmethods and impose excessive overheads for maintaining devices’***,data integrity and trust are another vital consideration for nextgeneration *** research proposed a load-balanced trusted surveillance routing model with collaborative decisions at network edges to enhance energymanagement and resource *** leverages graph-based optimization to enable reliable analysis of decision-making ***,mobile devices integratewith the proposed model to sustain trusted routes with lightweight privacy-preserving and *** proposed model analyzed its performance results in a simulation-based environment and illustrated an exceptional improvement in packet loss ratio,energy consumption,detection anomaly,and blockchain overhead than related solutions.

关键词： Smart cities load balancing blockchain health systems edge computing

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Weak Multi-Label Data Stream Classification Under Distribution Changes in Labels

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IEEE Transactions on Big Data 2025年第3期11卷 1369-1380页

作者： Zou, Yizhang Hu, Xuegang Li, Peipei Hu, Jun Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei230002 China National University of Singapore School of Computing 119077 Singapore

Multi-label stream classification aims to address the challenge of dynamically assigning multiple labels to sequentially arrived instances. In real situations, only partial labels of instances can be observed due to the expensive human annotations, and the problem of label distribution changes arises from multiple labels in a streaming mode, but few existing works jointly consider such challenges. Motivated by this, we propose the problem of weak multi-label stream classification (WMSC) and an online classification algorithm robust to weak labels. Specifically, we incrementally update the margin-based model using information from both the past model and the current incoming instance with partially observed labels. To increase the robustness to weak labels, we first adjust the classification margin of negative labels using the label causality matrix, which is constructed by the conditional probability of label pairs. Second, we introduce the label prototype matrix to regulate the margin by controlling the weighting parameter of the slack term. Additionally, to handle the potential distribution changes in labels, we utilize the instance-specific threshold via online thresholding to perform binary classification, which is formulated as a regression problem. Finally, theoretical analysis and empirical experimental results are presented to demonstrate the effectiveness of WMSC in classifying unobserved streaming instances. © 2015 IEEE.

关键词： Labeled data

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Automated AI-Powered Fruit Identification Using Convolutional Neural Network

Automated AI-Powered Fruit Identification Using Convolutiona...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Sai Tejasree, K. Surendran, R. Rajakumar, B. Gomathi, R.M. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India J.N.N Institute of Engineering Department of Artificial Intelligence and Data Science Chennai India School of Computing Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recognition. This method teaches the ML Model we clearly explained the Model using application's agent using PEAS and the application's task environment using the 6 dimensions by showing it tons of fruit images, so over time, it gets really good at spotting the differences and similarities between, say, a banana and a grape. We also used another technique called pattern recognition, which helps the computer pay attention to specific details like the fruit's color, shape, size and texture. Overcoming multiple obstacles in order to automatically identify the type of fruit from the picture. The variety of images is influencing the color, texture, and shape of many different types of fruits. When it came to fruit picture detection, Convolutional Neural Network (CNN) Algorithm outperformed standard support-vector-machine-based approaches using handcrafted features in terms of accuracy also, it is a lot faster to implement for new fruits. By integrating deep learning and pattern recognition techniques such as Convolutional Neural Network Algorithm we have got the accuracy of 84%, our system efficiently identified different fruit types from images, demonstrating the power and effectiveness of our methods. The goal of our project is to create a tool that can quickly and correctly identify many types of fruits in photos, which could be useful for things like sorting fruits in a grocery store or helping people learn about different fruits by using Convolutional Neural Network Algorithm. This is not just about teaching a computer to recognize fruits;it is about making technology that can understand and interact with the world in a way that is helpful to us. © 2025 IEEE.

关键词： Convolutional neural networks

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AG Codes Have No List-Decoding Friends: Approaching the Generalized Singleton Bound Requires Exponential Alphabets

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IEEE Transactions on Information Theory 2025年第4期71卷 2443-2451页

作者： Alrabiah, Omar Guruswami, Venkatesan Li, Ray University of California at Berkeley Department of Electrical Engineering and Computer Science BerkeleyCA94709 United States University of California at Berkeley Department of Electrical Engineering and Computer Science Department of Mathematics Simons Institute for the Theory of Computing BerkeleyCA94709 United States Santa Clara University Department of Mathematics and Computer Science Santa ClaraCA95053 United States

A simple, recently observed generalization of the classical Singleton bound to list-decoding asserts that rate R codes are not list-decodable using list-size L beyond an error fraction L/L+1 (1-R) (the Singleton bound being the case of L=1 , i.e., unique decoding). We prove that in order to approach this bound for any fixed L > 1 , one needs exponential alphabets. Specifically, for every L > 1 and R ∈ (0,1) , if a rate R code can be list-of-L decoded up to error fraction L/L+1 (1-R - ∈ ) , then its alphabet must have size at least exp (ΩL,R(1\ ∈)) . This is in sharp contrast to the situation for unique decoding where certain families of rate R algebraic-geometry (AG) codes over an alphabet of size O(1\ ∈2) are unique-decodable up to error fraction (1-R-\ ∈ )2 . Our bounds hold even for subconstant ∈ ≥ 1/n , implying that any code exactly achieving the L-th generalized Singleton bound requires alphabet size 2ΩL,R(n) . Previously this was only known only for L=2 under the additional assumptions that the code is both linear and MDS. Our lower bound is tight up to constant factors in the exponent—with high probability random codes (or, as shown recently, even random linear codes) over exp(OL(1\ ∈)) -sized alphabets, can be list-of-L decoded up to error fraction L/L+1 (1-R -∈ ) . © 1963-2012 IEEE.

关键词： Errors

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Out-of-Distribution Data: An Acquaintance of Adversarial Examples - A Survey

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ACM computing Surveys 2025年第8期57卷 1-40页

作者： Karunanayake, Naveen Gunawardena, Ravin Seneviratne, Suranga Chawla, Sanjay School of Computer Science The University of Sydney Sydney Australia Computer Science and Engineering University of New South Wales Sydney Australia Qatar Computing Research Institute Doha Qatar and Hamad Bin Khalifa University Doha Qatar

Deep neural networks (DNNs) deployed in real-world applications can encounter out-of-distribution (OOD) data and adversarial examples. These represent distinct forms of distributional shifts that can significantly impact DNNs’ reliability and robustness. Traditionally, research has addressed OOD detection and adversarial robustness as separate challenges. This survey focuses on the intersection of these two areas, examining how the research community has investigated them together. Consequently, we identify two key research directions: robust OOD detection and unified robustness. Robust OOD detection aims to differentiate between in-distribution (ID) data and OOD data, even when they are adversarially manipulated to deceive the OOD detector. Unified robustness seeks a single approach to make DNNs robust against both adversarial attacks and OOD inputs. Accordingly, first, we establish a taxonomy based on the concept of distributional shifts. This framework clarifies how robust OOD detection and unified robustness relate to other research areas addressing distributional shifts, such as OOD detection, open set recognition, and anomaly detection. Subsequently, we review existing work on robust OOD detection and unified robustness. Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework. © 2025 Copyright held by the owner/author(s)

关键词： Generative adversarial networks

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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Benchmarking Indian E-Commerce Giants: Insights from Festive Sales Performance

Benchmarking Indian E-Commerce Giants: Insights from Festive...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pranav, H. Harshini, Muppavarapu Sri Tarun, Rachuri Radha, D. Amrita School of Computing Dept of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331512965

This study presents a comparative analysis of the websites of three major e-commerce platforms in India: during the busy shopping sale of Diwali and Navratri. Using metrics derived from Lighthouse audits, the report evaluates the performance, accessibility, SEO, and adherence to best practices of each site. Key factors such as First Contentful Paint (FCP), Largest Contentful Paint (LCP), Speed Index, and overall user experience are examined. The analysis highlights the strengths and areas for improvement of each platform, with recommendations for enhancing load times, user accessibility, and search engine optimization. This comparison aims to provide actionable insights into optimizing the performance and usability of e-commerce websites. © 2025 IEEE.

关键词： Sales

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NC-Load: On-Demand Program Loading and Running for computing Sharing Among IoT Devices

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IEEE Internet of Things Journal 2025年第9期12卷 12242-12256页

作者： Dong, Yanhao Duan, Sijing Lyu, Feng Zhao, Peng Zhang, Yongmin Ren, Ju Zhang, Yaoxue Central South University School of Computer Science and Engineering Hunan Changsha410083 China Tsinghua University Department of Computer Science and Technology Beijing100084 China

The number of Internet of Things (IoT) devices has increased rapidly in recent years, but lack effective methods to integrate their computational power. In this article, we propose NC-Load, which couples IoT devices into a multiprocessor system, allowing process scheduling across different devices to share their computing power and improve overall throughput. Specifically, NC-Load consists of three key designs, i.e., remote page fault (RPF), lightweight program cropping, and identical memory layout migration, contributing to three merits compared to existing systems: 1) high storage efficiency: the target device launches the program with a locally stored lightweight icon and leverages RPFs to retrieve the required code/data from the source device;2) on-demand memory loading: only the required memory portions are transmitted when scheduling programs across different devices, which ensures quick recovery of the program;and 3) consistent memory layout: to ensure consistency of addresses after program offloading, the virtual memory area layout of the source device is migrated to the target device. We implement NC-Load on Linux 6.1 and conduct performance evaluation using unmodified programs and the N-Queens cases. The results demonstrate that NC-Load can achieve superior performance in terms of storage efficiency, program performance, memory usage, and throughput. © 2014 IEEE.

关键词： Multiprocessing systems

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Hybrid Approach for Electricity Price Forecasting using AlexNet and LSTM 3

Hybrid Approach for Electricity Price Forecasting using Alex...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sambana, Bosubabu Devi, Kotamsetty Geethika Reddy, Bandi Rajeswara Hussain, Galeti Mohammad Siddartha, Gownivalla School of Computing Mohan Babu University Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331527549

The recent development of advanced machine learning methods for hybrid models has greatly addressed the need for the correct prediction of electrical prices. This method combines AlexNet and LSTM algorithms, which are used to introduce a new model with higher accuracy in price forecasting. Despite RNN and ANN being effective, they often fail to deal with forex time sequence data. The traditional methods do not accurately forecast the prices. These traditional methods only focus on demand and price which leads to insufficient analysis of data. To address this issue, using the hybrid approach, which focuses on external variables that also effect the predicted prices. Nevertheless, due to AlexNet's excellent feature extraction and LSTM's learning sequential patterns, the prediction accuracy is vastly increased. The model is built on the past data, which has been supplied with the most significant elements like demand, temperature, sunlight, and rain. For example, the model applies methods, such as minimum-maximum scaling and a time window, to predict the electricity prices of the future. The results show that this hybrid model is good than the standalone ones in terms of accuracy. Although we got our accuracy rating of 97.08, it shows higher accompaniments than remaining models RNN and ANN with accuracies of 96.64 and 96.63 respectively. © 2025 IEEE.

关键词： Prediction models

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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