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ESSD: Energy Saving and Securing Data Algorithm for WSNs Security

computers, Materials & Continua 2022年第11期73卷 3969-3981页

作者： Manar M.Aldaseen Khaled M.Matrouk Laiali H.Almazaydeh Khaled M.Elleithy Computer Engineering Department Al-Hussein Bin Talal UniversityMa’anJordan Software Engineering Department Al-Hussein Bin Talal UniversityMa’anJordan Computer Science and Engineering Department University of BridgeportCTBridgeportUSA

The Wireless Sensor Networks(WSNs)are characterized by their widespread deployment due to low cost,but the WSNs are vulnerable to various types of *** defend against the attacks,an effective security solution is ***,the limits of these networks’battery-based energy to the sensor are the most critical impediments to selecting cryptographic ***,finding a suitable algorithm that achieves the least energy consumption in data encryption and decryption and providing a highly protected system for data remains the fundamental *** this research,the main objective is to obtain data security during transmission by proposing a robust and low-power encryption algorithm,in addition,to examining security algorithms such as ECC and MD5 based on previous *** this research,the Energy Saving and Securing Data algorithm(ESSD)algorithm is introduced,which provides the Message Digest 5(MD5)computation simplicity by modifying the Elliptic Curve Cryptography(ECC)under the primary condition of power *** three algorithms,ECC,MD5,and ESSD,are applied to Low Energy Adaptive Clustering Hierarchy(LEACH)and Threshold-sensitive Energy Efficient Sensor Network Protocol(TEEN)hierarchical routing algorithms which are considered the most widely used in *** results of security methods under the LEACH protocol show that all nodes are dead at 456,496,and 496,respectively,to ECC,MD5,and *** results of security methods under the TEEN protocol show that the test ends at 3743,4815,and 4889,respectively,to ECC,MD5,and *** on these results,the ESSD outperforms better in terms of increased security and less power *** addition,it is advantageous when applied to TEEN protocol.

关键词： Cryptography ECC energy consumption ESSD LEACH MD5 RSA TEEN WSNs

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AI-Powered Approaches to Unmasking Fake News: A Simple Comparison of ML and DL Techniques

AI-Powered Approaches to Unmasking Fake News: A Simple Compa...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Bormon, Sajib Prova, Jebin Ahmed Ahmad, Md Hasan Rimon Sohag, Sohanur Rahman Shishir, Hasanur Rahman Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

False news spreads quickly due to the extensive distribution of incorrect or misleading information across digital channels, which is a global problem. This bias undermines the credibility of information, promotes the spread of misleading information. Utilizing machine learning and deep learning models this study examines the detection of inaccurate information. Natural Language Processing methods are used for data preprocessing on the text datasets. Five machine learning models are used in this study: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Passive Aggressive Classifier. After preprocessing, the accuracy values of these models are 95%, 99.7%, 99.3%, 98.9%, and 99.5%, respectively. The study demonstrates that the decision tree model has superior performance, with the LSTM algorithm following closely after. The PAC and LSTM model have also demonstrated commendable results of 99.5% accuracy. The accuracies offer valuable information regarding the constraints of each model, aiding researchers in selecting suitable ways for identifying counterfeit news. The findings assist individuals in making informed decisions on the establishment of precise and effective ways for identifying false information, which is essential to maintaining the integrity of information distribution. © 2024 IEEE.

关键词： Adversarial machine learning

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Decentralizing Philippine Driver’s License Application and Identity Management for Transparency, Efficiency, and Security: A Design Proposal 4th

Decentralizing Philippine Driver’s License Application and...

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4th International Conference on Novel and Intelligent Digital Systems, NiDS 2024

作者： Lorico, Hans Daniel Q. Zamuco, Amiel David P. Caro, Jaime D. L. Juayong, Richelle Ann B. Service Science and Software Engineering Laboratory Department of Computer Science College of Engineering University of the Philippines Diliman Quezon City1101 Philippines

ISBN: (纸本)9783031733437

Blockchain is a developing and promising field in transaction and identity management. Recent efforts have been underway to address issues of data insecurity and inefficiency presented by centralized systems. Philippine government agencies, like the Land Transportation Office (LTO), traditionally store information in centralized databases, which can be susceptible to unauthorized access. This study makes the case of decentralizing certain LTO online procedures, starting with driver’s license application, as well as identity management using blockchain technology. With an immutable, distributed ledger system, it aims to introduce security, transparency, and efficiency into the system through a proof-of-concept web application. It demonstrates that not only certain LTO procedures can be decentralized, but also the management of license ID, more specifically the selective disclosure of information to requesting parties. The design presented here lays the groundwork for developing a decentralized application through Hyperledger Fabric and Indy, leveraging features like consensus mechanisms, verifiable credentials (VCs), and self-sovereign identity (SSI). The initial evaluation of the design using Hyperledger Caliper sets the stage for further research on decentralization, scalability, and efficiency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Efficiency

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Body Sensor-Based Multimodal Nurse Stress Detection Using Machine Learning 16

Body Sensor-Based Multimodal Nurse Stress Detection Using Ma...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mathur, Abhinav Shikha Sethia, Divyashikha Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9798350383119

Stress is a significant concern in the work-place, as it contributes to 80% of workplace injuries, and medical professionals are particularly susceptible, especially during emergencies such as the Covid-19 outbreak. Nurses play a crucial role in providing care in hospitals. Stress prediction for nurses in a hospital environment is essential to work optimally without suffering from the chronic effects of stress. This paper aims to investigate the application of body sensors and machine learning algorithms in monitoring physiological indicators to detect stress levels in nurses. It uses a multimodal sensor dataset of Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Inter-Beat Interval (IBI) signals from 15 nurses. The study trains and evaluates several classification models separately for each physiological signal and the multimodal data. Performance evaluation reveals that the decision tree outperforms other models with a weighted F1 score of 0.99 for both 2-level and 3-level multimodal stress classification. Furthermore, the IBI-derived Heart Rate Variability (HRV) features is more promising than other physiological signals, with a weighted F1 score of 0.84 and 0.79 for 2-level and 3-level stress classification, respectively. The findings show that multimodal data can help reliably detect stress for nurses and ensure stress-free working in hospital environments to reduce human errors. © 2024 IEEE.

关键词： Body sensors Heart Rate Variability Machine Learning Multimodal Stress Detection

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GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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computer Modeling in engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare 1

EdgeMob: A Context-Aware Dynamic Service Migration for Weara...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Raza, Imran Hussain, Syed Asad Latif, Sana COMSATS University Islamabad Lahore Department of Computer Science Lahore Pakistan Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350351736

Edge Computing (EC) is a distributed network architecture offering computation and storage resources at the network edge, addressing cloud computing limitations by optimizing latency and enhancing data safety and privacy for real-time services. It also supports intelligent solutions for IoT-based smart healthcare systems through Deep Learning (DL). DL aids in resource allocation optimization and disease prediction. However, the surge in wearable device usage introduces challenges in user mobility and migration, leading to increased latency and service inconsistencies. To tackle this, we propose EdgeMob, an intelligent and context-aware scheme deployed as a federation of edge servers. EdgeMob utilizes Q-learning-based reinforcement learning for personalized service migration decisions based on user mobility and Quality of Service (QoS). We evaluate our system against existing solutions, considering parameters such as user-perceived latency, migration cost, and network bandwidth utilization. © 2024 IEEE.

关键词： Electronic health record

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Classification Of Hookah Images With Deep Learning 8

Classification Of Hookah Images With Deep Learning

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Arslan, Mustafa Tahir Ozkaynak, Emrah Karabuk University Institute of Postgraduate Education Department of Computer Engineering Karabük Turkey Karabuk University Software Engineering Department Karabük Turkey

ISBN: (纸本)9798331531492

With the proliferation of social media usage, there has been a notable increase in posts related to hookah consumption, driven by its visually appealing presentation and distinctive consumption method. Such posts, especially in the context of the decreasing age of social media users, have the potential to exert an enticing influence. The potential impact of social media content on tobacco product consumption underscores the importance of identifying whether these posts contain tobacco-related imagery. Deep learning techniques have become increasingly prevalent in the detection of tobacco products within images and videos. However, existing studies have predominantly focused on cigarette detection due to its relatively higher consumption rates, leaving the area of hookah detection underexplored. In this study, the detection of hookah images in social media content was undertaken using the YOLO v5 algorithm, a version of convolutional neural networks, for the classification of hookah as an object in digital images. To achieve this, a dataset comprising 7200 hookah images of varying shapes, colors, and sizes was compiled from social media platforms and various e-commerce websites. The dataset was trained using the YOLO v5 xl model, and the experimental results indicated a detection accuracy of 98.88%. The findings of this experimental study indicate that the developed application could be employed to generate alerts or obscure hookah images in social media content. Consequently, this approach could contribute to raising awareness regarding the risks associated with hookah use and mitigating its potentially alluring effects. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring, Analyzing, and Benchmarking the Job Satisfaction Levels of Developers 8th

Exploring, Analyzing, and Benchmarking the Job Satisfaction ...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

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