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Revolutionizing Road Safety and Optimization with AI: Insights from Enterprise Implementation

International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1008-1019页

作者： Ouahbi, Younesse Ziti, Soumia Intelligent Processing and Security of Systems Informatics Department Faculty of Science Mohammed V University Rabat Morocco

This study explores the key factors influencing the adoption of artificial intelligence (AI) in the logistics sector, with a particular emphasis on road logistics management. It examines the technological, organizational, and environmental contexts that shape AI integration, as well as the challenges faced by logistics managers, including the need for digital transformation, carbon emissions reduction, and advanced parcel tracking management. The objective is to identify technological and human-related barriers to AI adoption and to assess the level of interest and readiness among logistics companies, especially in the Moroccan context. A quantitative research approach was adopted, based on an online survey targeting logistics professionals and decision-makers, mainly from European and Moroccan small and medium-sized enterprises (SMEs). The collected data were analyzed using statistical methods, including linear regression and ANOVA, to evaluate the relationships between company characteristics, perceived complexity of AI tools, and the availability of qualified human resources. The findings indicate that perceived complexity and limited access to specialized skills significantly hinder AI adoption. Moreover, the perception of tangible performance benefits—such as increased operational efficiency and reduced CO2 emissions—emerges as a major driver for acceptance. These insights offer practical implications for logistics companies seeking to leverage AI technologies to optimize operations, reduce environmental impact, and enhance parcel tracking systems. A strategic roadmap is proposed to overcome the identified barriers and promote effective AI integration. © (2025), (Science and Information Organization). All rights reserved.

关键词： Risk perception

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NW Logistics: System Architecture and Design for Sustainable Road Logistics

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 1096-1104页

作者： Younesse, Ouahbi Soumia, Ziti Intelligent Processing and Security of Systems-Informatics Department-Faculty of Science Mohammed V University Rabat Morocco

The logistics industry is under increasing pressure to reduce carbon emissions and enhance efficiency in response to environmental and regulatory demands. However, optimizing road logistics to achieve these goals requires innovative solutions that balance operational efficiency with sustain-ability. This study addresses this need by introducing NW Logistics, an AI powered platform that optimizes road logistics to lower CO2 emissions and improve fleet performance. In order to achieve these objectives, real-time CO2 tracking, route optimization, and driver behavior monitoring were integrated into NW Logistics. The system enables precise, real-time tracking of deliveries and vehicle locations, allowing logistics managers to monitor fleet performance with enhanced accuracy. Additionally, onboard cam eras and sensors generate individualized driver reports, tracking infractions and fostering safer driving behaviors. Initial simula tions of NW Logistics indicate a significant reduction in carbon emissions, along with improvements in route efficiency, delivery tracking ac-curacy, and driver safety. These results demonstrate the transformative potential of AI to advance sustainable and efficient logistics management. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Supply chain management

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The Role of Generative AI in Deepfake Detection: A Systematic Literature Review

The Role of Generative AI in Deepfake Detection: A Systemati...

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International Conference on Smart Business and technology, ICSBT 2024

作者： Acim, Btissam Kharmoum, Nassim Lagmiri, Souad Najoua Ziti, Soumia Department of Computer Science Intelligent Processing & Security of Systems Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco IT Networking Security and Management Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Intelligence (AI) does expand possibilities and give an opportunity to achieve terrific outcomes and has innumerable potential uses. This systematic literature review focuses on the role of generative AI in the improvement of deepfake detection technologies, providing a comparison between different methods and assessing their effectiveness. By analyzing recent studies, it depicts the techniques employed most frequently in deepfake detection, such as the ones concentrating on the intricacies between the visual and auditory elements, the temporal domains, as well as any minor signs of alteration. Due to the fact that generative AI is rapidly improving, the methods that are used to detect its use are often inadequate, which calls for more studies on the subject matter. Besides, by combining audio and visual elements when performing the analysis, calling it as a method that can be useful in focusing the detection growth potential with diverse and unstructured data. Also, this study evaluates the advantages and limitations of the techniques existing today and demonstrates their relative effectiveness in controlled and real conditions. Clarifying that measures aimed at detecting AI generated content should be preoccupied with a technique that is able to keep pace with changes in generative AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Data Migration from Relational to NoSQL Database: Review and Comparative Study

Data Migration from Relational to NoSQL Database: Review a...

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International Conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Saadouni, Chaimae El Bouchti, Karim Reda, Oussama Mohamed Ziti, Soumia Department of Computer Science Intelligent Processing Systems and Security Team Mohammed V University Rabat Morocco Algorithms Networks Intelligent Systems and Software Engineering Research Team Faculty of Sciences Mohammed V University Rabat Morocco

ISBN: (纸本)9783031263835

New storage requirements, analysis, and visualization of Big Data, which includes structured, semi-structured, and unstructured data, have caused the developers in the past decade to begin preferring Big Data databases, such as NoSQL, that comes to overcome the boundaries of relational Databases such as unlimited scalability, high performance, data modeling, data distribution, and continuous availability. Due to those benefits ensured by this new technology, quite recently, considerable attention has been paid to data migration from relational Database to NoSQL Database. So, in this context, the purpose of this paper is to provide a comparative study of some recent approaches proposed by various researchers to migrate data from relational to NoSQL databases, as well as discussing and analyzing the results of those proposed techniques. This research allowed us to gain a basic understanding of the contributions and approaches that have already been made, as well as the benefits and limits of each proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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BTC-Shadow: an analysis and visualization system for exposing implicit behaviors in Bitcoin transaction graphs

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Frontiers of Computer Science 2023年第6期17卷 167-169页

作者： Ding BAO Wei REN Yuexin XIANG Weimao LIU Tianqing ZHU Yi REN Kim-Kwang Raymond CHOO School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan 430074China NSFOCUS Technologies Group Co. Ltd.Beijing 100089China School of Computing Sciences University of East AngliaNorwich NR47TJUK Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX 78249-0631USA

1 Introduction A Bitcoin ledger comprises a sizable number of transaction records,which can be utilized to make it easier to track and analyze the traits and patterns of cryptocurrency-related *** facilitate the visual analysis of Bitcoin,numerous tools with various aims have been *** example,MiningVis[1]and SuPoolVisor[2]are the analytics systems for Bitcoin mining pools,as well as[3-5]focus on the Bitcoin transaction graphs ***,due to our previous research requirements for Bitcoin transaction graphs,none of the available tools can provide exploring the features of connection related to the address and observe significant visual ***,using these tools is challenging to navigate to abnormal node clusters effortlessly from large node groups and then analyze the local interlink characteristics between transaction nodes with interactive analytics.

关键词： analytic implicit utilized

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Modification of the Genetic Algorithm for Building and Reconfiguring Schedules of Order Completion in the Field of Service Provision 12

Modification of the Genetic Algorithm for Building and Recon...

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12th International Conference on Advanced Computer Information Technologies, ACIT 2022

作者： Hrybkov, Serhii Seidykh, Olga Vlasenko, Lidia Lytvynov, Valerii National University of Food Technologies Department of Information Systems Kyiv Ukraine National University of Food Technologies Department of Informatics Kyiv Ukraine National University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine Institute of Mathematical Machines and Systems Problems of Nasu Research Department № 220 'Intelligent Information-analytical System' Kyiv Ukraine

ISBN: (数字)9781665410502

ISBN: (纸本)9781665466479

The paper offers a solution to an important scientific and applied problem of scheduling to complete the orders for the provision of services aimed at maintenance and support of computer networks and equipment. This problem is relevant for companies providing such services. The main idea is to form an effective schedule for the completion of orders, as well as to enable its modification in the shortest time when getting orders. The paper also proposes an evaluation function that takes into account bonuses for the timely completion of orders and penalties for the late ones. A combination of a genetic algorithm with the methods of additional improvement on the basis of local optimization is offered. It allows reducing the time for the construction of effective alternative variants of decisions and their estimation. The use of the proposed approach to combining algorithms allows creating and reconfiguring the schedule for the completion of orders in the operational mode in a short time, and optimization makes it possible to reduce the completion of orders by up to 20%. Thus, the application of the proposed approaches will allow effective distribution of tasks among performers and give an economic effect by reducing the time to complete all orders. © 2022 IEEE.

关键词： Genetic algorithms

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Artificial Intelligence for Fake News 1

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International Conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Ennejjai, Imane Ariss, Anass Kharmoum, Nassim Rhalem, Wajih Ziti, Soumia Ezziyyani, Mostafa Department of Computer Science Intelligent Processing Systems and Security Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco E2SN Research Team ENSAM Rabat Mohammed V University in Rabat Rabat Morocco Mathematics and Applications Laboratory Faculty of Sciences and Techniques of Tangier Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031263842

ISBN: (纸本)9783031263835

Fake news is a severe problem on social media networks, with confirmed detrimental consequences for individuals and organizations. As a result, detecting false news is a significant difficulty. In this way, the topic of new fakes and their proper detection are crucial. General knowledge states that the receiver of information must verify the sources. However, the creation of new information can be a difficult problem that requires more than a single viewpoint based on a news source. The objective of this paper is to evaluate the performance of six deep learning models for fake news detection including CNN, LSTM, Bi-LSTM, HAN, Conv-Han and Bert. We find that BERT and similar pre-trained models perform the best for fake news detection, the models are described below with their experimental *** models are examined against ISOT [22, 30] datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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Machine Learning for Drug Efficiency Prediction 11th

Machine Learning for Drug Efficiency Prediction

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11th EAI International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2022

作者： Tiaiba, Hafida Sabri, Lyazid Chibani, Abdelghani Kazar, Okba Maths and Informatics Faculty Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj Algeria LISSI-The Laboratory of Images Signals and Intelligent Systems University Paris-Est Vitry-sur-Seine Île-de-France France LINFI Laboratory Computer Sciences Department University of Biskra Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates

ISBN: (纸本)9783031320286

Health-related social media data, particularly patients’ opinions about drugs, have recently provided knowledge for research on the adverse reactions, allergies that a patient experiences and drug efficacy and safety. We develop an effective method for analyzing medicines’ efficiency and conditions-specific prescription from patient reviews provided by Drug Review Dataset (***). Our approach relies on the Natural Language processing (NLP) principle and a word embedding vectorization method to preserve semantics. For this purpose, we conducted experiments using various sampling techniques, precisely random sampling and balanced random sampling. Furthermore, we applied several statistical models: Logistic Regression, Decision Tree, Random Forests, K-Nearest Neighbors (KNN) and Neural Network models (simple perceptron, multilayer perceptron and convolutional neural network). We varied the size of training and test data sets to study the effect of the sampling techniques on model efficiency. Compared to other models, the results show that the proposed models in this paper: KNN, Embedding-100, and CNN-Maxpooling outclass models proposed by several researchers. Indeed, Embedding-100 has achieved better training accuracy and test accuracy. Moreover, during our study, we concluded that different factors influence the effectiveness of the models, mainly the text preprocessing method, sampling techniques in terms of size and type, text vectorization method and machine learning models. © 2023, ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering.

关键词： Embeddings

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Tracking Methods: Comprehensive Vision and Multiple Approaches 1

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International Conference on Advanced intelligent systems for Sustainable Development, AI2SD 2022

作者： Ariss, Anass Ennejjai, Imane Kharmoum, Nassim Rhalem, Wajih Ziti, Soumia Ezziyyani, Mostafa Department of Computer Science Intelligent Processing Systems and Security Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco E2SN Research Team ENSAM Rabat Mohammed V University in Rabat Rabat Morocco Mathematics and Applications Laboratory Faculty of Sciences and Techniques of Tangier Abdelmalek Essaadi University Tétouan Morocco

ISBN: (数字)9783031352515

ISBN: (纸本)9783031352508

Tracking of human beings, as well as objects, becomes a great challenge currently presented, it aims to understand the fundamental principles of objects and human beings detected to associate them with robust systems for treaties and draw the desired conclusions. Tracking generally uses several methods such as graph theory, technological tools, Internet of Things (IoT), Big Data, and artificial intelligence, where it maintains several hypotheses to help tracking objects or human beings. In this article, we propose the tracking methods proposed in this direction, and finally, we analyze and discuss the various results obtained. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graph theory

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