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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Interpretive Structural Modeling of Uncertainty Reduction Factors in Social Commerce

Interpretive Structural Modeling of Uncertainty Reduction Fa...

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International Conference on Web Research (ICWR)

作者： Nasibeh Pouti Department of Information Technology and Computer Engineering Faculty of Engineering Sayyed Jamaleddin Asadabadi University Hamedan Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

As a new approach to online transactions, social commerce, like other online business models, suffers from uncertainty. The purpose of this research is to identify factors for reducing uncertainty in social commerce. However, considering the complex interactions of these factors; it is not enough to just identify them. In this research, an interpretive structural model of uncertainty-reducing factors was created. For this purpose, first, the factors extracted from the research literature were sifted by the scree test method, and 26 factors were confirmed. Then, during the implementation process of interpretative structural modeling, a model with 5 levels of importance was created. The final analysis was related to the classification of factors, based on which the identified factors were placed in two categories: independent and linkage, and no factor was included in the category of autonomous and dependent factors. The investigation showed that the independent factors are mostly located in the lower levels of the model, influence the reduction of uncertainty through other factors, while the linkage factors are mostly located in the upper levels of the model, and have a direct effect on the reduction of uncertainty. The leveling, determination of the type of impact, and determination of the complex relationships between uncertainty reduction factors, which were carried out in this research, have not been carried out in any similar research to date. The obtained 5-level model, the factors at each level and the complex relationships between them, will guide the development of successful business models in ecommerce and social commerce.

关键词： Hands Couplings Uncertainty Social networking (online) Biological system modeling Focusing Data science Electronic commerce Business Systematic literature review

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Child Tracking and Prediction of Violence on Children In Social Media Using Natural Language Processing and Machine Learning 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Nallakaruppan, M.K. Srivastava, Gautam Gadekallu, Thippa Reddy Reddy, Praveen Kumar Krishnan, Sivarama Polap, Dawid School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Mathematics and Computer Science Brandon University Brandon Canada School of Information Technology and Engineering Vellore Institute of Technology Department of Electrical and Computer Engineering Lebanese American University Lebanon Zhongda Group Zhejiang Jiaxing314312 China Faculty of Applied Mathematics Silesian University of Technology Kaszubska 23 Gliwice44-100 Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Crimes against children are a direct threat to the world of tomorrow. The future of the world is in the hands of children who are the real wealth of mankind. The challenge for society is to address and handle challenges against children such as sexual abuse, trafficking, child labour, and harassment. Even though measures are taken by several governments globally, violence against children is an evident day-to-day process. This becomes the most challenging issue in the post-Internet era, where organized crime against children takes place around the world through a targeted group of people through virtual private networks and the dark web. What comes next is the emergence of social media, which provides an opportunity for these criminals to harm children globally through powerful social networks [15]. The existing methods focus on classifiers that do not work across multiple keywords [6] to look for sentiment analysis regarding a single classifier. There are only a few works that address the issue and these methods do not employ the multi-classification model [2]. In this paper, we propose a model which includes the Twitter API, the APIFY web scraper framework, as well as the VADER semantic analyzer in Python which possesses Decision tree analysis of sentiment prediction. The proposed work provides 99.6904% accuracy and predicts the classification of sentiment analysis. Our work has novelty in attribute selection, child trade, and violence analysis in social media and prediction of classification accuracy using the Decision Tree algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Learning orientation detection system and its application to RGB images

Learning orientation detection system and its application to...

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2024 International Conference on Optics, Electronics, and Communication engineering, OECE 2024

作者： Chen, Tianqi Qiu, Zhiyu Hua, Yuxiao Todo, Yuki Tang, Zheng Division of Electrical Engineering and Computer Science Graduate School of Natural Science & Technology Kanazawa University Ishikawa Kanazawa920-1152 Japan Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa920-1192 Japan Faculty of Engineering University of Toyama Toyama930-8555 Japan

ISBN: (纸本)9781510685437

Our previous research showed that emulating dendritic neuron structure effectively addresses orientation detection challenges in learning tasks, reducing both learning time and costs compared to alternative neural network approaches. Our simulation model incorporates an On-Off Response mechanism in bipolar cells (BCs) and horizontal cells (HCs) for processing grayscale input images. To overcome limitations with single-channel input, we introduce color-selective cells. By integrating these cells, we enhance and select outputs of local orientation detection dendritic neurons, generating specific feature maps. These feature maps are generated using a biologically-inspired model that mimics the mechanism of color-selective cells. Additionally, global neurons are used to capture overall image features by aggregating outputs from local dendritic neurons. Our system utilizes backpropagation to update parameters of local orientation detection dendritic neurons. Furthermore, we integrate a learnable orientation detection neural network after the dendritic neuron stage. © 2024 SPIE.

关键词： RGB color model

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LiDAR Spoofing Attack Detection in Autonomous Vehicles

LiDAR Spoofing Attack Detection in Autonomous Vehicles

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Alheeti, Khattab M. Ali Alzahrani, Abdulkareem Al Dosary, Duaa College of Computer and Information Technology University of Anbar Computer Networking Systems Dept. Ramadi Iraq Albaha University Faculty of Computer Science and Information Technology Computer Engineering and Science Department Albaha Saudi Arabia College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9781665441544

Detecting 3D objects is a core task for autonomous vehicles (AVs), as it allows them to drive safely and responsibly. To detect objects quickly and accurately, AVs have LiDAR sensors, which can capture 3D data from 360° in various conditions, including harsh weather. However, LiDAR sensors may face a spoofing attack via laser satirizing. This type of attack does not require physical contact with the sensors nor does it need to change them. It can mislead AVs by providing them with false information, which can then put passengers, pedestrians, and other vehicles in danger. Therefore, this paper proposes a model utilizing machine learning (i.e., decision trees) to protect AVs from such attacks. The performance of the proposed model indicates that it can detect LiDAR spoofing attacks. © 2022 IEEE.

关键词： Autonomous vehicles

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Integrating Vehicular Clouds for Supporting Smart Transportation Infrastructure 4

Integrating Vehicular Clouds for Supporting Smart Transporta...

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4th International Conference on Smart Grid Metrology, SMAGRIMET 2023

作者： Balen, Josip Matijasevic, Marin Pejcic, Lovro Kundid, Josip Azeem, Muhammad Moazam Osijek Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia TTTech Auto Osijek Osijek Croatia Qatar University Department of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9798350323597

An emerging vehicular ad hoc network (VANET) is a network of connected vehicles which are communicating with each other and with roadside units (RSUs) exchanging useful information. As a result, road safety is increased, and complex traffic management is enabled. However, we are moving one step further and proposing the usage of vehicle resources, such as computing capabilities and data storage, for enabling vehicular clouds which can efficiently support smart transportation infrastructure. The main goal for establishing vehicular clouds is utilization of vast amounts of unused hardware resources available at vehicles for enabling and supporting smart transportation infrastructure. Vehicular clouds can enable additional distributed computing and storage resources to various groups of users, as well as introduce new use cases and applications. Recent vehicular cloud computing methods and models, as well as vehicular cloud computing architecture and services are explored in this paper. Based on the research and our work a new vehicular cloud computing architecture VANET-CC is proposed and elaborated. Furthermore, possible services for cloud computing are pointed out together with vehicle management in such clouds. © 2023 IEEE.

关键词： Vehicular ad hoc networks

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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion 10

Multi-Label Text Classification for Judicial Texts via Dual ...

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10th IEEE Smart World Congress, SWC 2024

作者： Gu, Qiliang Lu, Qin Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan China

ISBN: (纸本)9798331520861

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional Network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to 2. 2 8 % and a minimum of 1. 2 9 % across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model. © 2024 IEEE.

关键词： Semantics

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Opto-Video Encryption Based on Logistic Adjusted Sine map in FrFT

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computers, Materials & Continua 2022年第8期72卷 3651-3663页

作者： Osama S.Faragallah Ashraf Afifi Ibrahim F.Elashry Ensherah A.Naeem Heba M.El-Hoseny Ahmed I.Sallam Hala S.El-sayed Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Electrical Engineering Kafrelsheikh UniversityKafrelsheikh 61519Egypt Electrical Department Faculty of Industrial EducationSuez UniversitySuez 43527Egypt Department of Electronics and Electrical Communication Engineering Al-Obour High Institute for Engineering and Technology 3036Egypt Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf 32952Egypt Department of Electrical Engineering Faculty of EngineeringMenoufia UniversityShebin El-Kom 32511Egypt

In the last few years,videos became the most common form of information transmitted over the internet,and a lot of the traffic is confidential and must be protected and delivered safely to its intended *** introduces the challenges of presenting encryption systems that can encode videos securely and efficiently at the same *** paper presents an efficient opto-video encryption system using Logistic Adjusted Sine map(LASM)in the Fractional Fourier Transform(FrFT).In the presented opto-video LASM-based FrFT scheme,the encoded video is split into distinct frames and transformed into optical signals utilizing an optical *** of the developed optical video frames is ciphered by utilizing the LASM in optical FrFT system using two-phase modulation forms on the video frame,the first in the time-domain and the second in the FrFT *** the end,the ciphervideo frame is spotted utilizing a CCD digital camera and transformed into a digital structure that can be managed using a *** test the proposed opto-video LASM-based FrFT scheme using various security *** outcomes demonstrate that the presented scheme can effectively encrypt and decrypt video *** addition,it encrypts videos with a high level of encryption qualitywithout sacrificing its resistance to noise ***,the test outcomes demonstrate that the presented scheme is immune to known attacks.

关键词： Optical encryption FrFT logistic-adjusted sine map

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Minimizing energy consumption in fixed node networks using a novel neutrosophic model: comparative analysis with standard and existing algorithms

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International Journal of information technology (Singapore) 2025年 1-15页

作者： Ghimire, Sangam Bhurtel, Nirjal Jha, Sudan Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Department of Computer Science and Engineering Kathmandu University 28 kilo Bagmati Dhulikhel 45210 Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj 11942 Saudi Arabia School of Computer Science and Engineering Lovely Professional University Punjab Phagwara 144411 India Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Lebanon

The growing development and utilization of networked systems has led to more concern regarding the energy efficiency of these systems. In this paper, we present a novel approach to minimizing energy consumption in fixed node network for which we propose a new Neutrosophic model. This model leverages the unique strengths of Neutrosophic logic to enhance the efficiency by minimizing the energy consumption. The simulation of results is performed using Matplotlib (Python) with implementation parameters such as number of nodes (n) and additive white Gaussian noise (AWGN) (η). Additionally, we show a comparative analysis between the proposed model and other prevalent approaches used in minimizing energy consumption in networked systems. The comparative results shows improvement in energy efficiency, showcasing the potential of the proposed model as an alternative for optimizing energy consumption in networked systems. This work highlights the effectiveness of the proposed model and opens new avenues for future advancements in energy-efficient networked system using Neutrosophic approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Comparative analysis Energy-consumption optimization Networked systems Neutrosophic model Sensitivity analysis

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Qian, Heng Li, Chuan Li, Chengli Kunming University of Science and Technology Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming China School of Physics and Electronic Information Yunnan Normal University Yunnan Key Laboratory of Opo-electronic Information Technology Kunming China

ISBN: (纸本)9798350312614

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad2/Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method. © 2023 IEEE.

关键词： distributed optical fiber sensing laser phase noise phase demodulation phase-sensitive optical time-domain reflectometry

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