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Robust security risk estimation for android apps using nearest neighbor approach and hamming distance

Soft Computing 2025年第2期29卷 593-611页

作者： Deypir, Mahmood Zoughi, Toktam Faculty of Computer Engineering Shahid Sattari Aeronautical University of Science and Technology Tehran Iran Tehran Iran

Nowadays, Android-based devices such as smart phones, tablets, smart watches, and virtual reality headsets have found increasing use in our daily lives. Along with the development of various applications for these devices, new malicious apps are released by intruders, which are more difficult to identify and deal with because they exploit more sophisticated techniques. Although methods have been provided to calculate the security risk and identify malicious apps in Android operating system, but with the expansion of the level and depth of the threats, the need for more effective methods in this context is still required. In this paper, we have devised a new algorithm to calculate the security risk of Android apps, which can be used to identify malicious apps from benign ones. In this algorithm, to estimate the security risk of an unknown input app, its nearest neighbors to malicious apps and its nearest neighbors to normal apps are computed separately using Hamming distance. Then, the security risk of the input app can be computed using a simple formulation. After implementing this algorithm, its parameter for the number of neighbors using various real datasets is adjusted. The extensive experiments conducted on these data show the superiority of the proposed method over the previously proposed ones in terms of detection rate. Our additional experimentations show the robustness of the proposed algorithm in adversarial situations. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Android malware

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Revolutionizing cardiovascular health: integrating deep learning techniques for predictive analysis of personal key indicators in heart disease

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Neural Computing and Applications 2025年第1期37卷 1-24页

作者： Talaat, Fatma M. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science & Engineering New Mansoura University Gamasa35712 Egypt

Cardiovascular diseases (CVDs) remain a global burden, highlighting the need for innovative approaches for early detection and intervention. This study investigates the potential of deep learning, specifically convolutional neural networks (CNNs), to improve the prediction of heart disease risk using key personal health markers. Our approach revolutionizes traditional healthcare predictive modeling by integrating CNNs, which excel at uncovering subtle patterns and hidden interactions among various health indicators such as blood pressure, cholesterol levels, and lifestyle factors. To achieve this, we leverage advanced neural network architectures. The model utilizes embedding layers to transform categorical data into numerical representations, convolutional layers to extract spatial features, and dense layers to model complex interactions and predict CVD risk. Regularization techniques like dropout and batch normalization, along with hyperparameter optimization, enhance model generalizability and performance. Rigorous validation against conventional methods demonstrates the model’s superiority, with a significantly higher R2 value of 0.994. This achievement underscores the model’s potential as a valuable tool for clinicians in CVD prevention and management. The study also emphasizes the need for interpretability in deep learning models and addresses ethical considerations to ensure responsible implementation in clinical practice. © The Author(s) 2024.

关键词： Blood pressure

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Evolutionary recurrent neural network based on equilibrium optimization method for cloud-edge resource management in internet of things

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Neural Computing and Applications 2025年第6期37卷 4957-4969页

作者： Ebrahimi Mood, Sepehr Rouhbakhsh, Adel Souri, Alireza Department of Computer Science Yazd University Yazd Iran Department of Computer Engineering Faculty of Engineering Haliç University Istanbul34060 Turkey School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China

Cloud computing is an emerging field in information technology, enabling users to access a shared pool of computing resources. Despite its potential, cloud technology presents various challenges, with one of the most significant being resource management within the cloud environment. Specifically, the allocation of physical machines (PMs) to virtual machines (VMs) in the Infrastructure as a service (IaaS) layer aims to achieve optimal efficiency. To address this challenge, we propose an evolutionary recurrent neural network (RNN) for resource allocation, enhanced by the equilibrium optimization (EO) algorithm. This method improves the generalization performance and reduces the architecture complexity of RNNs, while ensuring better adaptability to various demand patterns in cloud environments. Additionally, the effectiveness of RNNs heavily relies on the quality of training data;hence, we generate appropriate training data to facilitate the development of an efficient resource management model. Through extensive simulations, our method demonstrates superior performance compared to traditional algorithms. However, it is worth noting that this study is primarily based on simulation results, highlighting the need for real-world validation to confirm its practical applicability. The proposed method's applicability to different cloud computing environments and scenarios, including various service models (IaaS, PaaS, and SaaS) and deployment models (public, private, and hybrid), is also briefly discussed. Future studies should explore the computational efficiency and potential optimization techniques of the EO-RNN approach and consider real-world implementations to validate its effectiveness and uncover practical challenges. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Cloud platforms

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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ST-KAN: Document Classification Using Sentence Transformer and the Kolmogorov-Arnold Network 29

ST-KAN: Document Classification Using Sentence Transformer a...

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29th International Conference on Information Technology, IT 2025

作者： Göz, Furkan University of Kocaeli Faculty of Engineering Computer Engineering Kocaeli41001 Turkey

ISBN: (纸本)9798331517649

The study introduces a novel approach to scientific document classification by integrating Sentence Transformer (ST) embeddings with the Kolmogorov-Arnold Network (KAN). Unlike Multi-layer Perceptron (MLP) with fixed activation functions, KAN utilizes learnable activation functions on edges. In this study, the arXiv Categories dataset is used for evaluation. The dataset contains titles, abstracts of documents, and category labels. The proposed method first merges the title and abstract into a single text representation. This representation is then converted into embeddings using the all-MiniLM-L6-v2 ST. Finally, KAN is applied as the classification model to learn from these embedding representations. Experiments are conducted on the main categories and subcategories to compare performance of ST-KAN against FastText-KAN, FastText-MLP, and ST-MLP. The results show that ST-KAN achieves better performance. © 2025 IEEE.

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Deep learning and supervised learning techniques for modeling and prediction of strength of ground granulated blast furnace slag based sustainable mortar

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Neural Computing and Applications 2025年第3期37卷 1709-1726页

作者： Gürsoy-Demir, Handan Ozturk, Murat Department of Computer Engineering Iskenderun Technical University Hatay Iskenderun31200 Turkey Faculty of Engineering Department of Civil Engineering Bursa Uludag University Bursa Turkey

This paper presents an analysis and synthesis of deep learning and supervised learning techniques for predicting the flexural tensile strength and compressive strength of mortars containing ground granulated blast furnace slag (GGBFS). The goal is to save time and reduce the costs associated with experimental testing. Initially, mechanical tests were conducted experimentally, using GGBFS as a partial replacement for cement in the mortar, with replacement levels ranging from 0 wt% to 80 wt% in 1% intervals. The results indicated a nearly linear decrease in flexural tensile strength and a non-linear change in compressive strength with increasing GGBFS content. To address the limitations of traditional experimental methods and improve processes, deep learning and supervised learning approaches were explored and compared. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared demonstrated that the deep learning model achieved higher sensitivity and efficiency for both 7-day and 28-day flexural tensile strength, as well as 7-day and 28-day compressive strength. Quantitatively, the deep learning model outperformed traditional models for 28-day flexural tensile strength, with an RMSE of 0.6779, an R-squared value of 0.90, an MSE of 0.4596, and an MAE of 0.6045, and for 28-day compressive strength, with an RMSE of 0.6808, an R-squared value of 0.99, an MSE of 0.4636, and an MAE of 0.4891. These findings suggest that deep learning is a promising method for accurately modeling and predicting the mechanical properties of GGBFS-based mortars, particularly for the 28-day strengths. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Mortar

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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A Review on the Use of Smart Agriculture Systems to Protect Crops 3

A Review on the Use of Smart Agriculture Systems to Protect ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Kumar, Gulshan Gourshettiwar, Palash Suman, Saurabh Kumar, Shravan Kumar, Dhiraj Raushan, Rahul Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331509675

Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started

关键词： Smart agriculture

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Predicting Student Behavior Using a Neutrosophic Deep Learning Model

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Neutrosophic Sets and Systems 2025年 76卷 288-310页

作者： Shitaya, Ahmed Mohamed Wahed, Mohamed El Syed Abd El Khalek, Saied Helemy Ismail, Amr Shams, Mahmoud Y. Salama, A.A. Department of Mathematics and Computer Science Faculty of Science Port Said University Egypt Department of Computer Science Faculty of computer and information sciences Suez Canal University Egypt Department. of Communication and Computer Engineering Air Defense University Egypt Department of Cybersecurity College of Engineering and Information Technology Buraydah Private Colleges Buraydah51418 Saudi Arabia Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt

We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory—a mathematical framework that accounts for truth, falsity, and indeterminacy—with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. © 2025, University of New Mexico. All rights reserved.

关键词： Contrastive Learning

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Optimizing Stand-Alone PV Systems:A Metaheuristic-Enhanced Fuzzy Approach for Adaptive MPPT

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China Communications 2025年第1期22卷 61-74页

作者： Tina Samavat Mostafa Nazari Lin Fuhong Lei Yang Department of Computer Science and Engineering University of NevadaRenoNVUSA Faculty of Mechanical Engineering Shahrood University of TechnologyShahroodIran School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.

关键词： genetic algorithm imperialist competitive algorithm invasive weed algorithm maximum power point tracking

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