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AI-powered dining: text information extraction and machine learning for personalized menu recommendations and food allergy management

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International Journal of Information technology (Singapore) 2025年第4期17卷 2107-2115页

作者： Brahimi, Samiha Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia

Individuals with food allergies face limitations in social events and restaurant dining. Artificial intelligence solutions should be offered to this category. In this paper, a recommender system is proposed for the benefit of people with food allergies. The system aims to identify convenient options for the user in a restaurant/hotel menu. The system collects user’s allergy information and the restaurant menu, it extracts dishes names using a machine learning model. Then it conducts search about the recipes of these dishes and identify allergen-free ones. The system has been implemented as a mobile application involving a Naïve Bayes classification model and a web search API. The performance of the classifier was significant (accuracy 87%). Yet, an enhancement approach was introduced to increase the accuracy to 90%. In addition, an expert-driven test has been conducted and 98.5% of the system allergen identification was accurate in comparison with the original recipes used by restaurants’ chefs. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Information retrieval Machine learning Menu recommendation Recommender systems

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Spatio-Temporal Learning for Route-Based Travel Time Estimation

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Journal of computer science & technology 2024年第5期39卷 1107-1122页

作者： Zi-Quan Fang Qi-Chen Sun Lu Chen Dan-Lei Hu Yun-Jun Gao School of Software Technology Zhejiang UniversityNingbo 315048China College of Computer Science Zhejiang UniversityHangzhou 310027China

Travel time estimation(TTE)is a fundamental task to build intelligent transportation ***,most existing TTE solutions design models upon simple homogeneous graphs and ignore the heterogeneity of traffic networks,where,e.g.,main roads typically contribute differently from side *** terms of spatial dimension,few studies consider the dynamic spatial correlations across road segments,e.g.,the traffic speed/volume on road segment A may correlate with the traffic speed/volume on road segment B,where A and B could be adjacent or non-adjacent,and such correlations may vary across *** terms of temporal dimension,even fewer studies consider the dynamic temporal dependences,where,e.g.,the historical states of road A may directly correlate with the recent state of A,and may also indirectly correlate with the recent state of road *** track all aforementioned issues of existing TTE approaches,we provide HDTTE,a solution that employs heterogeneous and dynamic spatio-temporal predictive ***,we first design a general multi-relational graph constructor that extracts hidden heterogeneous information of road segments,where we model road segments as nodes and model correlations as edges in the multi-relational ***,we propose a dynamic graph attention convolution module that aggregates dynamic spatial dependence of neighbor roads to focal *** also present a novel correlation-augmented temporal convolution module to capture the influence of states at past time steps on current traffic ***,in view of the periodic dependence of traffic,we develop a multi-scale adaptive fusion layer to enable HDTTE to exploit periodic patterns from recent,daily,and weekly traffic *** experimental study using real-life highway and urban datasets demonstrates the validity of the approach and its advantage over others.

关键词： heterogeneous traffic modeling spatio-temporal learning travel time estimation traffic speed prediction

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A Dynamic Clustering Method for Maximizing Energy Efficiency in C-V2X Metaverse 10

A Dynamic Clustering Method for Maximizing Energy Efficiency...

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10th IEEE Smart World Congress, SWC 2024

作者： Ma, Mingyang Feng, Xin Zhang, Jing Liu, Haohua Wang, Jiacheng Sun, Geng Changchun University of Science and Technology College of Computer Science and Technology Changchun China Nanyang Technological University College of Computer Science and Engineering Singapore Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798331520861

The Metaverse can leverage intelligent traffic management technology to simulate the Cellular Vehicle-to-Everything (C-V2X) environment, integrating closely with the Internet of Vehicles due to its advanced connectivity and real-time data processing capabilities. These features make it possible to create a smart transportation system that closely mirrors the real world. Dynamic clustering is an effective network management method for handling the large number of mobile vehicles within the Metaverse, particularly in emergency scenarios where base stations (BSs) may fail. However, frequent changes in network topology due to high-speed vehicles result in unstable clusters and low communication efficiency. In this study, a dynamic clustering algorithm based on motion vector similarity (DA-MVS) is proposed for constructing stable clusters, and an adaptive adjustment clusters strategy is presented based on the change rate of vehicle numbers to improve energy efficiency (EE). Furthermore, we propose an elite chaotic perturbed sparrow search algorithm (ECPSSA) to optimize the EE of the C-V2X. Compared to the multiple baseline algorithm, the successful transmission rate can be improved by up to 76.9%, and the EE is enhanced by 20.97%. © 2024 IEEE.

关键词： Information management

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Enhanced DDoS Detection Using Advanced Machine Learning and Ensemble Techniques in Software Defined Networking

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computers, Materials & Continua 2024年第11期81卷 3003-3031页

作者： Hira Akhtar Butt Khoula Said Al Harthy Mumtaz Ali Shah Mudassar Hussain Rashid Amin Mujeeb Ur Rehman Department of Computer Science University of Management and TechnologySialkot51040Pakistan Department of Computer Science and Creative Technologies Global College of Engineering and TechnologyMuscat2546Sultanate of Oman Department of Computer Science University of WahWah Cantt47040Pakistan Department of Computer Science and IT University of ChakwalChakwal48800Pakistan

Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern *** detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)*** Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent *** this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN *** model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant *** adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack *** proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble *** proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in *** provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving *** comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing *** results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.

关键词： Table 1(continued)OSI layer Possible DDoS attack Data link MAC Address Flooding Physical Cable disconnection JaMMING physical impersonation

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Multiclass Classification for Cyber Threats Detection on Twitter

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computers, Materials & Continua 2023年第12期77卷 3853-3866页

作者： Adnan Hussein Abdulwahab Ali Almazroi College of Computer Science and Engineering Department of Computer ScienceAL-Ahgaff UniversityMukallaYemen College of Computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddahSaudi Arabia

The advances in technology increase the number of internet systems *** a result,cybersecurity issues have become more *** threats are one of the main problems in the area of ***,detecting cybersecurity threats is not a trivial task and thus is the center of focus for many researchers due to its *** study aims to analyze Twitter data to detect cyber threats using a multiclass classification *** data is passed through different tasks to prepare it for the *** Frequency and Inverse Document Frequency(TFIDF)features are extracted to vectorize the cleaned data and several machine learning algorithms are used to classify the Twitter posts into multiple classes of cyber *** results are evaluated using different metrics including precision,recall,F-score,and *** work contributes to the cyber security research *** experiments revealed the promised results of the analysis using the Random Forest(RF)algorithm with(F-score=81%).This result outperformed the existing studies in the field of cyber threat detection and showed the importance of detecting cyber threats in social media *** is a need for more investigation in the field of multiclass classification to achieve more accurate *** the future,this study suggests applying different data representations for the feature extraction other than TF-IDF such as Word2Vec,and adding a new phase for feature selection to select the optimum features subset to achieve higher accuracy of the detection process.

关键词： Cybersecurity cyber threat detection artificial intelligence machine learning Twitter

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Transfer Learning Empowered Skin Diseases Detection in Children

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computer Modeling in Engineering & sciences 2024年第12期141卷 2609-2623页

作者： Meena N.Alnuaimi Nourah S.Alqahtani Mohammed Gollapalli Atta Rahman Alaa Alahmadi Aghiad Bakry Mustafa Youldash Dania Alkhulaifi Rashad Ahmed Hesham Al-Musallam Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia ICS Department King Fahd University of Petroleum and MineralsDhahran31261Saudi Arabia

Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)*** left untreated,these diseases can have severe consequences and spread,especially among *** detection is crucial to prevent their spread and improve a patient’s chances of ***,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and *** study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the *** method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and *** proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,*** illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.

关键词： Deep learning MobileNet DenseNet121 skin diseases detection transfer learning

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Mitigating Blackhole and Greyhole Routing Attacks in Vehicular Ad Hoc Networks Using Blockchain Based Smart Contracts

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computer Modeling in Engineering & sciences 2024年第2期138卷 2005-2021页

作者： Abdulatif Alabdulatif Mada Alharbi Abir Mchergui Tarek Moulahi Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Higher Institute of Management of GabesGabes UniversityGabes6029Tunisia Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Faculty of Science and Technology of Sidi Bouzid Kairouan UniversityKairouan3131Tunisia

The rapid increase in vehicle traffic volume in modern societies has raised the need to develop innovative solutions to reduce traffic congestion and enhance traffic management *** advanced technology,such as Intelligent Transportation systems(ITS),enables improved traffic management,helps eliminate congestion,and supports a safer *** provides real-time information on vehicle traffic and transportation systems that can improve decision-making for road ***,ITS suffers from routing issues at the network layer when utilising Vehicular Ad Hoc Networks(VANETs).This is because each vehicle plays the role of a router in this network,which leads to a complex vehicle communication network,causing issues such as repeated link breakages between vehicles resulting from the mobility of the network and rapid topological *** may lead to loss or delay in packet transmissions;this weakness can be exploited in routing attacks,such as black-hole and gray-hole attacks,that threaten the availability of ITS *** this paper,a Blockchain-based smart contracts model is proposed to offer convenient and comprehensive security mechanisms,enhancing the trustworthiness between ***-Classification Blockchain-Based Contracts(SCBC)and Voting-Classification Blockchain-Based Contracts(VCBC)are utilised in the proposed *** results show that VCBC succeeds in attaining better results in PDR and TP performance even in the presence of Blackhole and Grayhole attacks.

关键词： Blockchain data privacy machine learning routing attacks smart contract VANET

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Different-Mode Power Splitters for Optical Testing of Three-Channel and Dual-Mode Waveguide Crossing

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Photonic Sensors 2025年第2期15卷 110-118页

作者： Hansi MA Te DU Xinpeng JIANG Zhaojian ZHANG Xin HE Huan CHEN Yang YU Zhenfu ZHANG Yunxin HAN Junbo YANG Yuanxi PENG Center of Material Science National University of Defense TechnologyChangsha 410073China College of Computer National University of Defense TechnologyChangsha 410073China School of Microelectronics and Physics Hunan University of Technology and BusinessChangsha 410205China

We study that the different-mode(waveguide-connected)power splitter[(W)PS]can provide different-mode testing points for the optical *** the PS or WPS providing two different-mode testing points,the measured insertion losses(ILs)of the three-channel and dual-mode waveguide crossing(WC)for both the fundamental transverse electric(TE0)and TE1 modes are less than 1.8 dB or 1.9 dB from 1540 nm to 1560 *** the same time,the crosstalks(CTs)are lower than-17.4 dB or-18.2 *** consistent test results indicate the accuracy of the(W)PS-based testing ***,combining the tunable tap couplers,the(W)PS can provide multiple testing points with different modes and different transmittances.

关键词： Different-mode(waveguide-connected)power splitter testing points three-channel and dual-mode waveguide crossing

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Track Damage And Obstacle Detection Using Multi Sensor Fusion Framework 15

Track Damage And Obstacle Detection Using Multi Sensor Fusio...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Priya, S.Padma Abarna, P.S. Devi, C.Manjula Hammed, S. Shahul Karthikeyan, M.P. Saravanan, K.Muthu Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Velammal college of Engineering and Technology Department of Information Technology Madurai India

ISBN: (纸本)9798350370249

The integration of Internet of Things (IoT) technology into railway track monitoring systems marks a pivotal advancement in ensuring the safety and efficiency of railway operations. Through strategic deployment of sensors along the tracks, this innovative system offers real-time detection of obstacles, furnishing vital data for timely intervention to avert potential accidents. This capability not only elevates passenger and crew safety but also bolsters the reliability of railway transportation systems as a whole. The system constantly scans the track ahead of the train for any anomalies using sensors positioned toward the front of the vehicle. The system immediately notifies the application faults it detects, enabling prompt action. Additionally, it updates the status of an IoT platform and displays it for personnel on board via an LCD panel. An audio alarm is generated in the event of critical sensor readings to ensure prompt attention. By lowering potential risks and responding quickly to track problems, This IoT-based solution holds the promise of revolutionizing railway operations by furnishing augmented safety, efficiency, and reliability, thereby addressing the burgeoning demands of contemporary transportation infrastructure. © 2024 IEEE.

关键词： InfraRed Sensor Railway safety reliability track monitoring Ultrasonic Sensor

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A Support Vector Machine(SVM)Model for Privacy Recommending Data Processing Model(PRDPM)in Internet of Vehicles

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computers, Materials & Continua 2025年第1期82卷 389-406页

作者： Ali Alqarni Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Open networks and heterogeneous services in the Internet of Vehicles(IoV)can lead to security and privacy *** key requirement for such systems is the preservation of user privacy,ensuring a seamless experience in driving,navigation,and *** privacy needs are influenced by various factors,such as data collected at different intervals,trip durations,and user *** address this,the paper proposes a Support Vector Machine(SVM)model designed to process large amounts of aggregated data and recommend privacy preserving *** model analyzes data based on user demands and interactions with service providers or neighboring *** aims to minimize privacy risks while ensuring service continuity and *** SVMmodel helps validate the system’s reliability by creating a hyperplane that distinguishes between maximum and minimum privacy *** results demonstrate the effectiveness of the proposed SVM model in enhancing both privacy and service performance.

关键词： Support vector machine big data IoV privacy-preserving

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