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Informatica (Slovenia) 2024年第6期48卷 173-184页

作者： Awadh, Wid Akeel Hashim, Mohammed S. Alasady, Ali Salah Department of Computer Information Systems University of Basrah Basrah Iraq Department of Computer science Education College for Pure Sciences University of Basrah Basrah61004 Iraq Department of Computer Science University of Basrah Basrah Iraq

Recently, big data analysis has been a very active research area with a significant impact on industrial and scientific domains. Thus, the security of big data provides the conditions for securing and monitoring cloud applications that need protect highly sensitive data hosted in cloud platforms. Nevertheless, the big data security issues have become increasingly problematic, leading organizations to restrict the utilization of cloud services. The existing security methods have revealed several issues, including a weakness of data security and Inaccuracy in data analysis, inefficiencies in performance and dependence on a third party. To address these issues, this study proposed a simpler technique as known a triple-data encryption standard (3DES) through providing the long keys sizes in Data Encryption Standard (DES) to protect the privacy of data against the potential attack in cloud computing environments. The experimental results confirm the efficiency of the proposed method in enhancing the secure of big healthcare data storage in cloud computing environment with less computational time compared to the existing method of Intelligent Framework for Healthcare Data Security. In conclusion, the proposed 3DES is recommended as a candidate encryption and decryption method in healthcare applications in cloud computing environments. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Sensitive data

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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi Department of Computer Science Alhamd Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Technology University of Science and Technology BeijingBeijing100083China Department of Computer Science National University of TechnologyIslamabad44000Pakistan Department of Electrical Engineering National University of TechnologyIslamabad44000Pakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical *** the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also *** researchers worked on different algorithms to protect cloud data from replay *** of the papers used a technique that simultaneously detects a full-message and partial-message replay *** study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay *** program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original *** the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the *** mechanism has the benefit of enhancing the detectability of replay ***,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy *** the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

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Hotspotter: An Incentivized Crowdsensing System for WiFi and Cellular Network Coverage Visualization 29

Hotspotter: An Incentivized Crowdsensing System for WiFi and...

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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Ortiz, Sebastian Luis S. Enriquez, Justin Gabriel R. Tan, Wilson M. Festin, Cedric Angelo M. University of the Philippines - Diliman Networks and Distributed Systems Laboratory Department of Computer Science Metro Manila Philippines

ISBN: (纸本)9798331517816

Reliable internet access is a key enabler for economic growth. Although the Philippine government launched initiatives to improve connectivity, connection speeds remained below the global average, especially for mobile networks. This paper presents Hotspotter, a system that aims to aid implementers and stakeholders in quantifying and addressing network coverage issues. Hotspotter is an incentivized crowdsensing system that collects, maps, and visualizes WiFi and cellular data to pinpoint hotspots and dead zones for the effective deployment and relocation of WiFi access points. A mobile application for Android was developed to facilitate data collection on geolocation, nearby WiFi access points, and connected cellular networks. The user interface visualizes the aggregated data in a hexagon-grid map. Fieldwork was conducted in two sitios of Barangay San Lorenzo, Norzagaray, Bulacan to stress-test the Hotspotter system in a Geographically Isolated and Disadvantaged Area (GIDA). It was found that 2G and 4G had the widest coverage and strongest signals overall, with modal signal strengths of 4.0 and 3.0, respectively. Being at the cutting edge, 5G was not yet supported. In the end, the mobile application's passive sensing, collecting, and caching of data successfully operated even in the most isolated areas without an internet connection. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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SEASHA3: Secure and Efficient Encryption for the IoT Data by Replacing Subkeys of AES with SHA3

SEASHA3: Secure and Efficient Encryption for the IoT Data by...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Soni, Ankush Sahay, Sanjay K. Bits Pilani Department of Computer Science and Information Systems Goa India

ISBN: (纸本)9781665410205

Advanced Encryption Standard (AES) is one of the most widely used cipher for data encryption. However, recent studies have pointed out some weaknesses in the subkeys used in AES. Also, the sequential subkeys generation process of AES seems to be inefficient for resource-constrained IoT devices. Therefore, in this paper, we studied how secure and efficient the Secure Hash Algorithm-3 (SHA3) generated subkeys will be for the encryption of IoT data by just replacing the AES subkeys with it. For this purpose we used the statistical test suite provided by the National Institute of Standards and Technology and found that SHA-3 generated subkeys are highly random, and also it is significantly efficient for encrypting the IoT Data. However, as symmetric keys are repeatedly used, therefore, only upto 2 MB data, the encryption is significantly (∼ 1300 times) efficient. From the experimental analysis, we find that SEASHA3 generates subkeys that are random, non-linear, and irreversible, making it more secure and efficient than traditional AES subkey generation. © 2024 IEEE.

关键词： Encryption algorithms

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SkinMarkNet: an automated approach for prediction of monkeyPox using image data augmentation with deep ensemble learning models

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Multimedia Tools and Applications 2025年第18期84卷 20177-20193页

作者： Akram, Aqsa Jamjoom, Arwa A. Innab, Nisreen Almujally, Nouf Abdullah Umer, Muhammad Alsubai, Shtwai Fimiani, Gianluca Bahawalpur Pakistan Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Riyadh Diriyah13713 Saudi Arabia 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 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 Computer Science University of Salerno Fisciano84084 Italy

Monkeypox, a rare but potentially fatal viral disease, poses a significant public health challenge due to its potential for outbreaks and complications. Detecting monkeypox lesions early and accurately is vital to effectively manage and control the disease. This study introduces a novel method for classifying monkeypox lesions, employing data augmentation methods and a framework based on an ensemble of three transfer learning models called "SkinMarkNet". The dataset used in this research consists of skin lesion images collected from the Kaggle data repository, encompassing diverse demographics and lesion characteristics. This research uses image data augmentation techniques to tackle the scarcity of annotated data. This augmentation enriches the training dataset, thereby improving the model’s ability to perform effectively. Moreover, the novelty of this research work lies in the usage of three popular transfer learning models(Inception, Xception, and ResNet) for feature extraction and ensemble learning. The SkinMarkNet achieves promising results showing an accuracy of 90.615% for monkeypox lesion classification, outperforming traditional machine learning and deep learning methods utilized in recent research works. In addition, thorough comparative analysis is done with machine learning models and contemporary approaches to validate the efficacy of the proposed method. Overall, the findings underscore the potential of leveraging advanced deep learning architectures and data augmentation strategies for improving monkeypox lesion classification, thereby facilitating early diagnosis and intervention in clinical settings and public health surveillance efforts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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Revisiting recommender systems: an investigative survey

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Neural Computing and Applications 2025年第4期37卷 2145-2173页

作者： Ibrahim, Osman Ali Sadek Younis, Eman M. G. Mohamed, Ebtsam A. Ismail, Walaa N. Computer Science Department Minia University Minia Egypt Information Systems Department Minia University Minia Egypt Management Information system Department Al-Yamamah University Riyadh Saudi Arabia

This paper provides a thorough review of recommendation methods from academic literature, offering a taxonomy that classifies recommender systems (RSs) into categories like collaborative filtering, content-based systems, and hybrid systems. It examines the effectiveness and challenges of these systems, such as filter bubbles, the "cold start" issue, and the reliance on collaborative filtering and content-based approaches. We trace the development of RSs, emphasizing the role of machine learning and deep learning models in overcoming these challenges and delivering more accurate, personalized, and context-aware recommendations. We also highlight the increasing significance of ethical considerations, including fairness, transparency, and trust, in the design of RSs. The paper presents a structured literature review, discussing various aspects of RSs, such as collaborative filtering, personalized recommender systems, and strategies to improve system robustness. It also points out the limitations of the existing approaches and suggests promising research directions for the future. In summary, this paper offers a comprehensive analysis of RSs, focusing on their evolution, challenges, and potential future improvements, particularly in enhancing accuracy, diversity, and ethical practices in recommendations. © The Author(s) 2025.

关键词： Collaborative filtering

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Exploring the Efficacy of a Local-Language Interfaced ChatGPT as A Self-Learning Support Tool for Junior High School Students in Rural Communities in Africa 9

Exploring the Efficacy of a Local-Language Interfaced ChatGP...

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9th IEEE International Conference on Adaptive science and Technology, ICAST 2024

作者： Glover-Tay, Senam Afi Ayorkor Korsah, G. Ashesi University Computer Science and Information Systems Department Berekuso Ghana

ISBN: (纸本)9798350385403

Large language models have been found to be useful in teaching and learning. Unfortunately, these models are not usually built on low-resource languages like many of the local languages spoken in Ghana and other West African countries. Using the Twi language, spoken in many parts of Ghana, as a case study, this research sought to answer the question, "Does a local language interface on ChatGPT make it a good learning support tool for junior high school students in rural communities in Africa?"The main objectives were to develop a Twi-interfaced ChatGPT that facilitates interaction in Twi and to assess students' acceptance of various interaction modalities, namely typing and speech. Students from a junior high school in a rural community of Ghana interacted with the standard ChatGPT chatbot, ChatGPT with an English speech interface, the proposed system with a Twi speech interface, and a simulated system in which a human played the role of a Twi interface. The case study illustrates that the concept of a local language interface to ChatGPT as a learning tool is promising: although the performance of the system currently falls short of what is needed, there is user acceptance of the concept, which could work well in the near future as speech recognition and text-to-speech systems for low-resource languages approach human-level accuracy. © 2024 IEEE.

关键词： Students

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Deepfake detection using convolutional vision transformers and convolutional neural networks

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Neural Computing and Applications 2024年第31期36卷 19759-19775页

作者： Soudy, Ahmed Hatem Sayed, Omnia Tag-Elser, Hala Ragab, Rewaa Mohsen, Sohaila Mostafa, Tarek Abohany, Amr A. Slim, Salwa O. Department of Computer Science Faculty of Computers and Artificial Intelligence Helwan University Cairo Egypt Department of Information Systems Faculty of Computers and Information Kafrelsheikh University Kafrelsheikh Egypt

Deepfake technology has rapidly advanced in recent years, creating highly realistic fake videos that can be difficult to distinguish from real ones. The rise of social media platforms and online forums has exacerbated the challenges of detecting misinformation and malicious content. This study leverages many papers on artificial intelligence techniques to address deepfake detection. This research proposes a deep learning (DL)-based method for detecting deepfakes. The system comprises three components: preprocessing, detection, and prediction. Preprocessing includes frame extraction, face detection, alignment, and feature cropping. Convolutional neural networks (CNNs) are employed in the eye and nose feature detection phase. A CNN combined with a vision transformer is also used for face detection. The prediction component employs a majority voting approach, merging results from the three models applied to different features, leading to three individual predictions. The model is trained on various face images using FaceForensics++ and DFDC datasets. Multiple performance metrics, including accuracy, precision, F1, and recall, are used to assess the proposed model’s performance. The experimental results indicate the potential and strengths of the proposed CNN that achieved enhanced performance with an accuracy of 97%, while the CViT-based model achieved 85% using the FaceForences++ dataset and demonstrated significant improvements in deepfake detection compared to recent studies, affirming the potential of the suggested framework for detecting deepfakes on social media. This study contributes to a broader understanding of CNN-based DL methods for deepfake detection. © The Author(s) 2024.

关键词： Face recognition

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Modern Standard Arabic speech disorders corpus for digital speech processing applications

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International Journal of Speech Technology 2024年第1期27卷 157-170页

作者： Alqudah, Assal A. M. Alshraideh, Mohammad A. M. Abushariah, Mohammad A. M. Sharieh, Ahmad A. S. Department of Computer Science King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan

Digital speech processing applications including automatic speech recognition (ASR), speaker recognition, speech translation, and others, essentially require large volumes of speech data for training and testing purposes. Although there are available speech corpora, speech data for speakers suffering speech disorders are hardly available for many languages including Arabic language. Consequently, developing digital speech processing applications that target the entire society becomes hard due to the unavailability of speech corpora that contain sufficient speakers’ variations including healthy and disordered speech. This research presents our work towards developing a Modern Standard Arabic (MSA) speech corpus for speakers suffering distortion and substitution articulation disorders. The speech corpus was recorded by 40 (20 male and 20 female) Jordanian speakers who suffer either distortion or/and substitution articulation disorders. This speech corpus can be used for various applications including ASR, speech and hearing, and others. Part of this speech corpus is used for developing and evaluating an ASR for MSA using the Carnegie Mellon University (CMU) Pocketsphinx tools based on Mel-Frequency Cepstral Coefficients (MFCC) and Hidden Markov Model (HMM) techniques. Furthermore, Linear Discriminant Analysis (LDA) and Maximum Likelihood Linear Transform (MLLT) optimization techniques were applied. Using three different testing data sets, this work obtained 98.38% and 1.76% average word recognition correctness rate (WRCR) and average Word Error Rate (WER), respectively, for speaker-dependent and text-independent. For speaker-independent and text-dependent, this work obtained 99.37% and 0.68% average WRCR and average WER, respectively, whereas for speaker-independent and text-independent this work obtained 96.53% and 4.00% average WRCR and average WER, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natur

关键词： Hidden Markov models

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Improving Continuous Emotion Annotation in Video Platforms via Physiological Response Profiling 12

Improving Continuous Emotion Annotation in Video Platforms v...

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12th International Conference on Affective Computing and Intelligent Interaction, ACII 2024

作者： Banik, Swarnali Sen, Sougata Saha, Snehanshu Ghosh, Surjya BITS Pilani Department of Computer Science and Information Systems Goa India

ISBN: (纸本)9798331516437

Many video applications (e.g., gaming, meeting, tutoring) aim to improve the user's interaction experience based on continuously inferred user emotion. To infer user emotion, these apps typically deploy machine learning models, trained with continuously collected emotion ground truth labels. However, as continuous annotations are generally collected as emotion self-reports during video consumption (using an auxiliary device), they incur significant annotation effort. To address this problem, we propose PResUP, a framework that creates users' profile using physiological responses (e.g., GSR or galvanic skin re-sponse) and deploys an LSTM network to identify the opportune probing moments for emotion ground truth (emotion self-report) collection instead of continuous annotation. We evaluate the proposed approach on a large-scale publicly available dataset (CASE) containing the physiological signals of subjects during video consumption. The evaluation of PRes UP reveals that it reduces the probing rate by 30.3 % (on average), detects the opportune probing moments with a TPR (True Positive Rate) of 86.1 %, and yet maintains the quality of the emotion annotations as observed in the continuous self-reports. Furthermore, we evaluated the generalizability of PResUP on another public dataset (K-emocon);which reveals an average probing rate reduction of 25.71 %. These results underscore the efficiency of PResUP in reducing the continuous emotion annotation overhead. © 2024 IEEE.

关键词： Sensory perception

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