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A Systematic Literature Review of Food Recommender Systems

SN computer science 2024年第1期5卷 174页

作者： Mahajan, Pratibha Kaur, Pankaj Deep Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India Computer Science and Engineering Department Guru Nanak Dev University Regional Campus Jalandhar India

The enormous variations in food choices and lifestyle in today’s world have given rise to the demand of using recommender system as a suitable tool in making appropriate food choices. Need for choosing nutritious food items is becoming important in today’s modern lifestyle as people are getting indulged in eating unhealthy food and thus leading to miserable health status. Many lifestyle-related diseases, such as diabetes and obesity, can also be reversed by proper diet. Filtering techniques in recommender system use a dataset of items and user’s preferences as input to discover a list of well-judged items as suggestions. In light of the above said, this paper presents an exploratory study of the recommendation approaches and methods utilized for food and recipe recommendations in the past decade. For this purpose, several relevant papers published in the food domain from 2006 to 2023 have been extensively studied. Further, the research articles are categorized based on the filtering techniques, methods, functionalities and data sources used by the researchers. A comparative study of the recommendation approaches revealed the advantages and disadvantages of different approaches. Also, the paper emphasizes the importance of food recommendation techniques in health and nutrition management of an individual. The review highlights the need to further explore the implementation of recommender systems in the food industry. Furthermore, the findings of this survey provide researchers with insights and future directions on recommender systems in the domain. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Food Food/Recipe/Menu recommender system Information retrieval Menu Recipe Recommender system

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Power Minimization Beamforming Design for Active RIS-Assisted ISAC System 16

Power Minimization Beamforming Design for Active RIS-Assiste...

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16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Yin, Lei Jin, Ming Li, Yaming Liu, Yinhong Chen, Chaodi Zhang, Huifang Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331506322

This paper investigates the active reconfigurable intelligent surfaces (RIS)-assisted integrated sensing and communication (ISAC) system, in which a dual-functional base station (BS) simultaneously transmits communication information and senses radar targets. Specifically, we consider a challenging practical scenario where the channel state information (CSI) and the radar reflection coefficient are not fully available. Under this limitation, the communication outage probability and target angle estimation failure probability constraints are introduced to describe the robust beamforming design. We aim to minimize the system's total power consumption by jointly optimizing the BS transmit beamformers and the active RIS phase-shifting. To address the non-convex optimization problem, we use the BernsteinType Inequality to safely approximate probability constraints. Then, we use the alternate optimization (AO) algorithm and semidefinite relaxation (SDR) technique to solve subproblems iteratively. Numerical results verify the active RIS-assisted ISAC system can significantly alleviate 'multiplicative fading' and reduce the power consumption compared to benchmark schemes. © 2024 IEEE.

关键词： Convex optimization

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

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Intelligent Automation & Soft Computing 2024年第1期39卷 1-13页

作者： M.L.Maruthi Prasad R.Santhosh Department of Computer Science and Engineering Faculty of EngineeringKarpagam Academy of Higher EducationCoimbatoreTamil NaduIndia

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women *** major research problem is the lack of automated model to attain earlier *** existing model fails to give better prediction ***,a novel clinical decision support system is framed to make the proper decision during a time of *** stages are followed in the proposed framework,which plays a substantial role in thyroid *** steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier *** classifiers aremerged to handle the issues identified in others and intend to enhance the prediction *** proposed model provides superior outcomes and gives good quality prediction *** simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing *** model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

关键词： Thyroid machine learning pre-processing classification prediction rate

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Joint Modeling of Citation Networks and User Preferences for Academic Tagging Recommender System

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computers, Materials & Continua 2024年第6期79卷 4449-4469页

作者： Weiming Huang Baisong Liu Zhaoliang Wang Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315211China Inner Mongolia Metal Material Research Institute Baotou014000China

In the tag recommendation task on academic platforms,existing methods disregard users’customized preferences in favor of extracting tags based just on the content of the ***,it uses co-occurrence techniques and tries to combine nodes’textual content for *** still do not,however,directly simulate many interactions in network *** order to address these issues,we present a novel system that more thoroughly integrates user preferences and citation networks into article labelling ***,we first employ path similarity to quantify the degree of similarity between user labelling preferences and articles in the citation ***,the Commuting Matrix for massive node pair paths is used to improve computational ***,the two commonalities mentioned above are combined with the interaction paper labels based on the additivity of Poisson *** addition,we also consider solving the model’s parameters by applying variational *** results demonstrate that our suggested framework agrees and significantly outperforms the state-of-the-art baseline on two real datasets by efficiently merging the three relational *** on the Area Under Curve(AUC)and Mean Average Precision(MAP)analysis,the performance of the suggested task is evaluated,and it is demonstrated to have a greater solving efficiency than current techniques.

关键词： Collaborative filtering citation networks variational inference poisson factorization tag recommendation

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Enhancing Software Cost Estimation Using Feature Selection and Machine Learning Techniques

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computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

Software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Design an optimization based ensemble machine learning framework for detecting rice leaf diseases

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Multimedia Tools and Applications 2024年第36期83卷 84401-84424页

作者： Rajasekhar, Veeramreddy Arulselvi, Gnanasekaran Babu, K. Suresh Department of Computer Science Engineering Annamalai University Tamilnadu Chidambaram India Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Tamil Nadu Chidambaram India International School of Technology and Sciences for Women East Gonagudem Rajanagaram A.P Rajamahendravaram India

The agriculture industry is currently dealing with serious issues with rice plants as a result of illnesses that decrease the quantity and output of the harvest. Numerous fungi and bacteria diseases harm plants that affect the growth of yield. So identification of the detection of crop diseases is essential for enhancing the productivity of crops. Many techniques have been developed to improve disease detection in crops but there are still some issues such as noise, time consumption, delay, poor segmentation, and less accuracy. To overcome these issues, this paper, designed a Spider Monkey-based Random Forest (SMbRF) model for accurate detection and classification of rice leaf diseases and the designed model was implemented in the Python system. The main aim is to develop an optimization-based random forest technique for accurate prediction of rice leaf disease by exact segmentation. Initially, preprocessing is performed to enhance the data quality and remove errors. Furthermore, separate the lesions using mean shift image segmentation and extract the shape and color features to improve the detection results of the developed model. As well, spider monkey fitness is updated in the RF classification layer for accurate classification of diseases and improves accuracy with less execution time. The experiment results show the reliability and efficiency of the designed model, it attains better performance in accuracy, sensitivity, specificity, and precision. The designed model attained 99.29% accuracy, 99.52% sensitivity, 98.76% precision, and 99% specificity proving the scalability of the designed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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Improving Accuracy Performance on CNN and BiLSTM Algorithms for Classification of Public Complaints Against Police Through WhatsAPP 8

Improving Accuracy Performance on CNN and BiLSTM Algorithms ...

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8th International Conference on Information Technology, Information Systems and Electrical engineering, ICITISEE 2024

作者： Oktarino, Ade Defit, Sarjon Yuhandri Adiwangsa Jambi University Faculty of Engineering and Computer Science Jambi City Indonesia Universitas Putra Indonesia 'YPTK' Faculty of Computer Science Padang Indonesia

ISBN: (纸本)9798350368970

The Indonesian republic police have provided services to the community by facilitating reporting via WhatsApp. However, messages sent through WhatsApp require manual identification to determine the type of offense reported by the public. Therefore, this research aims to assist in this process by applying deep learning approaches, specifically Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). Despite the high accuracy of CNN and BiLSTM in some classifications, their performance varies depending on the dataset. This study enhances both the architecture and data preprocessing, including class balancing using SMOTE. To prevent overfitting, early stopping is applied during training. The optimization process in this study utilizes the Adam optimizer, known for its efficiency in handling large-scale data and its ability to adapt the learning rate. The experimental results indicate that while early stopping effectively mitigates overfitting, the resulting accuracy of 68% is not optimal. Without early stopping, the accuracy improves to 91 % for CNN and 98% for BiLSTM. These findings confirm that stopping models at the right moment and the choice of data augmentation and split techniques significantly impact the overall accuracy, making them crucial aspects of this research. © 2024 IEEE.

关键词： Convolutional neural networks

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Hybrid lightweight encryption for IoT: integrating chaotic key generators with Feistel and substitution-permutation networks for secure 3DV transmission

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Multimedia Tools and Applications 2025年 1-47页

作者： El-Shafai, Walid Mesrega, Ahmed K. Ahmed, Hossam Eldin H. Elbahnasawy, Nirmeen A. El-Samie, Fathi E. Abd Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Deptartment of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The Internet of Things (IoT) stands as a revolutionary leap in digital connectivity, envisioning a future network connecting billions of devices, seamlessly. Amidst the myriad benefits, there arises an intricate web of challenges, prominently centered around potential threats and data security implications. Recent cryptography techniques, such as DNA-based cryptography, 3D chaos-based cryptography, and optical cryptography, face challenges including large encryption times, high energy consumption, and suboptimal rather than optimal performance. Particularly, the burden of long encryption cycles strains the energy resources of typical low-power and compact IoT devices. These challenges render the devices vulnerable to unauthorized breaches, despite large storage capacities. The hallmark of the IoT ecosystem, characterized by its low-power compact devices, is the burgeoning volume of data they generate. This escalating data influx, while necessitating expansive storage, remains vulnerable to unauthorized access and breaches. Historically, encryption algorithms, with their multifaceted architectures, have been the bulwark against such intrusions. However, their inherently-complex nature, entailing multiple encryption cycles, strains the limited energy reserves of typical IoT devices. In response to this intricate dilemma, we present a hybrid lightweight encryption strategy. Our algorithm innovatively leverages both one-dimensional (1D) and two-dimensional (2D) chaotic key generators. Furthermore, it amalgamates a classical encryption philosophy, harmonizing the strengths of Feistel and substitution-permutation networks. The centerpiece of our strategy is achieving effective encryption in merely three rounds, tailored expressly for compressed Three-Dimensional Video (3DV) frames, ensuring their unwavering integrity. Our workflow commences with the H.264/MVC compression algorithm, setting the stage for the subsequent encryption phase. Through rigorous MATLAB simulations,

关键词： Image coding

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Secure authentication scheme with Archimedes optimization algorithm for load balancing technique in fog computing

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3861-3869页

作者： Premkumar, N. Santhosh, R. Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India

In this paper, to develop an efficient secure authentication scheme and load balancing technique in fog computing. To achieve an efficient secure authentical scheme in addition load balancing method in fog computing, Hybrid Edge DataCenters (HEDC) is developed in this paper. Normally, the EDC can be utilized to set up as a distributed system in addition it is located among the data source and cloud datacentre. This EDC network is also placed in the intermediate layer in the fog hierarchy among cloud datacentres in addition Internet of Things (IoT). This EDC is operated as an intermediate layer in fog computing which is responsible for load balancers and secure authentication in distributing the workload. This EDC fails to achieve efficient secure authentication and manage the workload in fog computing. So, the proposed HEDC is utilized to enable efficient authentication and workload management (load balancing). The proposed method is a combination of EDC and Archimedes Optimization Algorithm (AOA). Based on resource utilization and response time, efficient load balancing is achieved. Additionally, the secure authentication scheme is also attained with the assistance of the HEDC. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Archimedes optimization algorithm Data access control EDC Fog computing Internet of things Security enhancement

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