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Informatica (Slovenia) 2025年第1期49卷 207-220页

作者： Bhatt, Arpita Jadhav Sardana, Neetu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Indonesia

In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for user’s privacy, have increased. information on smartphones is perhaps, more personal than compared to data stored on desktops or computers, making it an easy target for intruders. After Android, the most prevalently used mobile operating system is Apple’s iOS. Both Android and iOS follow permission-based access control to protect user’s privacy. However, the users are unaware whether the app is breaching the user’s privacy. To combat this problem, in the paper we propose a hybrid approach to detect malicious iOS apps based on its permissions. In the first phase, weights have been assigned to app permissions using multi-criteria decision-making (MCDM) approach namely Analytic Hierarchy Process (AHP), and in the second phase machine learning& ensemble learning techniques have been employed to train the classifiers for detecting malicious apps. To test the efficacy of the proposed method dataset comprising 1150 apps from 12 app categories has been used. The results demonstrate the proposed approach improves the efficacy of detecting malicious iOS apps for majority of categories. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Differential privacy

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Exploration of alzheimer disease using design of experiments

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Multimedia Tools and Applications 2025年第3期84卷 1079-1101页

作者： Gupta, Pranav Pandey, Kavita Gupta, Sanskar Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

This study examines the use of experimental designs, specifically full and fractional factorial designs, for predicting Alzheimer’s disease with fewer variables. The full factorial design systematically investigates the effects of selected factors, providing a comprehensive analysis of their individual and interactive impacts on Alzheimer’s prediction. On the other hand, the fractional factorial design simplifies the model by selecting key factors and removing non-significant ones. For Alzheimer’s, among all the factors examined, the MMSE (Mini-mental state Examination) score emerged as the most significant, which was further cross-validated using Minitab Software Analysis. Importantly, the findings suggest that the fractional factorial design is a better alternative, particularly when time and cost constraints are present. The results highlight the effectiveness and efficiency of these experimental designs, demonstrating their potential for early detection, prevention, and treatment of Alzheimer’s disease. By contributing to the expanding knowledge base on experimental designs in medical research, this study emphasizes their crucial role in understanding and combating complex diseases like Alzheimer’s. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Design of experiments

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An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

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Recent Advances in computer science and Communications 2024年第2期17卷 1-12页

作者： Pandey, Kavita Chandak, Abhimanyu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the im-portant findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance. © 2024, Bentham science Publishers. All rights reserved.

关键词： Losses

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Global-local term fusion for optimised community Q&A topic modelling

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International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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An Improved Graph Partitioning Algorithm Based Approach for Workflow Offloading in a Fog Environment

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 623-634页

作者： Mahajan, Neetu Narang Kaur, Parmeet Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflows locally and hence, need to offload these tasks to fog nodes. As compared to cloud-based servers, fog nodes can provide responses in a more-timely manner and are preferred for latency-sensitive applications. Workflow applications are characterized by inter-task dependencies and hence, can be readily represented as directed acyclic graphs. Therefore, the proposed offloading solution approach utilizes an improved graph partitioning algorithm based on the Louvain community detection algorithm. The aim of the algorithm is to partition the workflow graph in such a manner that the workflow tasks having high communication costs between them are transferred or offloaded to the same fog node. The benefits of the proposed algorithm have been verified by simulation experiments where it was observed that it results in a lower makespan as compared to the related approaches. © The Institution of Engineers (India) 2024.

关键词： Fog

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Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

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Journal of Internet technology 2024年第3期25卷 455-463页

作者： Ho, Wen-Jen Hsieh, Hsin-Yuan Tsai, Chia-Wei Software Technology Institute Institute for Information Industry Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation. © 2024 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Energy conservation

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DNACDS:Cloud IoE big data security and accessing scheme based on DNA cryptography

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Frontiers of computer science 2024年第1期18卷 157-170页

作者： Ashish SINGH Abhinav KUMAR Suyel NAMASUDRA School of Computer Engineering KIIT Deemed to be UniversityBhubaneshwar 751024India Department of Computer Science and Engineering Indian Institute of Information Technology SuratSurat 394190India Department of Computer Science and Engineering National Institute of Technology AgartalaAgartala 799046India

The Internet of Everything(IoE)based cloud computing is one of the most prominent areas in the digital big data *** approach allows efficient infrastructure to store and access big real-time data and smart IoE services from the *** IoE-based cloud computing services are located at remote locations without the control of the data *** data owners mostly depend on the untrusted Cloud Service Provider(CSP)and do not know the implemented security *** lack of knowledge about security capabilities and control over data raises several security *** Acid(DNA)computing is a biological concept that can improve the security of IoE big *** IoE big data security scheme consists of the Station-to-Station Key Agreement Protocol(StS KAP)and Feistel cipher *** paper proposed a DNA-based cryptographic scheme and access control model(DNACDS)to solve IoE big data security and access *** experimental results illustrated that DNACDS performs better than other DNA-based security *** theoretical security analysis of the DNACDS shows better resistance capabilities.

关键词： IoE based cloud computing DNA cryptography IoE big data security StS KAP feistel cipher IoE big data access

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An Integrated Framework with Enhanced Primitives for Post-Quantum Cryptography: HEDT and ECSIDH for Cloud Data Security and Key Exchange

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

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Modern Machine Learning Solution for Electricity Consumption Management in Smart Buildings

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IEEE engineering Management Review 2025年第1期53卷 54-62页

作者： Gautam, Sandeep Kumar Shrivastava, Vinayak Udmale, Sandeep S. Singh, Amit Kumar Singh, Sanjay Kumar Department of Computer Science and Engineering Varanasi221005 India Department of Computer Engineering and Information Technology Mumbai400019 India National Institute of Technology Department of Computer Science and Engineering Patna800005 India

Effective management of electricity consumption (EC) in smart buildings (SBs) is crucial for optimizing operational efficiency, cost savings, and ensuring sustainable resource utilization. Accurate EC prediction enables proactive decision-making, ensuring that resources are allocated efficiently to meet actual demand levels while maintaining occupant comfort. Population growth, building expansion, and technology usage swiftly escalate electricity demand, thus necessitating economical EC management strategies and assist consumers to better understand and strategically plan their EC. To address these challenges, this article proposes a novel approach based on a hybrid prediction model combining temporal convolutional networks (TCNs) and gated recurrent units (GRU). This approach capitalizes on the strengths of both TCN and GRU. TCN is adept at efficiently identifying diverse patterns, particularly within the complex working environments of SBs, by effectively capturing high- and low-frequency information. Subsequently, GRU is leveraged to address the long-term dependencies within the data, enhancing the accuracy of EC prediction. In this article, the results demonstrate the effectiveness of the proposed hybrid model, outperforming competitive methods with an impressive mean absolute error score. This underscores the potential of this approach to improve energy management practices significantly within SB environments, ultimately enhancing both operational efficiency and occupant satisfaction. © 1973-2011 IEEE.

关键词： Forecasting

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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