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Personalized Smart Grocery Delivery with IoT technology Using Collaborative Filtering Algorithms 11

Personalized Smart Grocery Delivery with IoT Technology Usin...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Kulkarni, Shirish Dattatreya Raman, Ramakrishnan Sutaria, Kruti Kamal Mishra, Nilamadhab Sujatha, S. Nandagopal, V. Maharashtra Pune India Maharashtra Pune India Parul Institute of Engineering and Technology Parul University Faculty of Engineering and Technology Gujarat Vadodara India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India School of Engineering Mohan Babu University Department of Electrical and Electornics Engineering Andrapradesh Tirupathi India

ISBN: (纸本)9798350350357

Personalized suggestions may enhance the use of online food shopping, an already popular and handy service. But data sparsity and scalability problems restrict the current recommendation systems rely on user-based collaborative filtering algorithms. Smart grocery delivery with personalized suggestions is proposed in this system using item-based collaborative filtering algorithms and Internet of Things (IoT) technologies. In the system, smart appliances like fridges and scales track food supplies and consumption, and then, according to the user's preferences, they automatically place purchases on internet platforms. It makes use of item-based collaborative filtering algorithms to examine other users' evaluations and purchase histories in order to provide the user with relevant and varied product recommendations. The method is tested on a real-world dataset and contrasted with collaborative filtering methods rely on user input. Techniques outperform the competition in terms of suggestion accuracy, variety, and coverage while simultaneously decreasing delivery time and costs. By implementing our strategy, not only can online grocery shopping be made more enjoyable for users, but it can also spur the growth of e-commerce and the IoT. © 2024 IEEE.

关键词： Food supply

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Weighted Salp Swarm Algorithm to Minimize Congestion in Smart City Environment 4

Weighted Salp Swarm Algorithm to Minimize Congestion in Smar...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Sathish Kumar, G. Sivasankar, C. Rathinam, Anantha Raman Latha, P. Gojan School of Business and Technology Department of Information Technology Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Kl University Department of Computer Science and Engineering -Honors Andhra Pradesh Vijayawada India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

Rapid urban population growth increases various issues, including lengthy traffic congestion and pollution which makes city living unsafe and uninhabitable. Applications for smart cities go beyond just using technology to make cities better and the lives of their inhabitants better. However, it can develop complete solutions for the growth of sustainable communities. This article introduces a Weighted Salp Swarm algorithm to minimize Congestion (WSSC) in vehicular ad-hoc networks. The Slap Swarm algorithm is employed in this article to improve the network performance by switching from one vehicle to nearby vehicles to send data packets efficiently. The minimal use of vehicle's energy guarantees the network's maximum lifespan. The main objective is to reduce congestion and prevent packet loss while minimizing data transmission delays under competing conditions. The performance comparison of WSSC with other existing approaches shows that the efficiency of WSSC in terms of throughput and lifetime is improved than others. © 2023 IEEE.

关键词： Smart city

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HDLCP: Experimental Analysis and Development of Hybrid Deep Learning Methodology for Crime Scenario Assesment and Prediction 1

HDLCP: Experimental Analysis and Development of Hybrid Deep ...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Tamilselvi, M. Ravi, C.N. Jayasudha, S. Shahad, P. Karthikeyan, S. Samuthira Pandi, V. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India CMR Engineering College Department of Computer Science and Engineering Hyderabad Medchal India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Tamil Nadu Chennai India Andhrapradesh Madanapalle India KSR College of Engineering Department of ECE Tamil Nadu Tiruchengode India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361780

For a variety of reasons, prediction is utilized in nearly every industry. Many societal functions, like as crime prediction, are served by it. Data mining tools abound when it comes to traditional prediction. These approaches lack accuracy when dealing with new kinds of data and are somewhat outdated. They take a lot of time as well. In place of the antiquated data mining methods, Artificial Neural Networks function well. This study employs a Hybrid Deep Learning based Crime Prediction (HDLCP) model to forecast criminal activity;the model is then tested for accuracy using the Decision Tree (DT) technique for cross-validation. Using the current datasets, additional information is anticipated to be extracted. Criminal activity is perilous and pervasive, affecting societies all across the globe. Life expectancy, GDP growth, and national prestige are all impacted by crime rates. New methods and sophisticated technologies are required to enhance crime analytics in order to safeguard communities and ensure the safety of society as a whole. A number of crime probabilities in a certain area may be studied, detected, and predicted by the suggested approach. Using a number of data mining approaches, the author explains the many forms of criminal study and crime prediction. © 2024 IEEE.

关键词： Forecasting

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Identification of selection criteria for blockchain platforms

Identification of selection criteria for blockchain platform...

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2022 International Conference on Frontiers of Communications, Information System and Data science, CISDS 2022

作者： Muradi, Shahr Banoo Kia Chiam, Yin Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665464468

Blockchain technology is widely used to develop software systems in different industries such as finance, healthcare, supply chain management, data management, Internet of Things (IoT). To adopt blockchain, some critical challenges such as performance, interoperability, availability, security attacks, sustainability, and latency need to be taken into consideration. If the blockchain platform selected is not appropriate, the functionality and quality of the blockchain-based systems will be affected. Additionally, numerous blockchain platforms with unique properties and specifications are available in the market, and the task of selecting the right platform that best meets the project needs is getting complicated. It is crucial to define significant criteria that can help to shortlist the blockchain platforms for further comparison and selection using multicriteria decision-making methods. This research proposes a set of selection criteria that cover both features and quality attributes of the blockchain platforms. A survey was conducted to get the opinions of blockchain practitioners on the suitability of the selection criteria for the evaluation and selection of blockchain platforms. © 2022 IEEE.

关键词： Blockchain

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Performance Comparison of Object Detection Neural Network Models Based on Accuracy and Latency 2

Performance Comparison of Object Detection Neural Network Mo...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Gomathy, B. Sengottaiyan, N. Aarthi, K. Thirumoorthy, P. Tamizharasu, K. Kalyanasundaram, P. Dr.NGP Institute of Technology Dept of Computer Science and Business Systems Tamilnadu Coimbatore India Alliance College of Engineering and Design Alliance University Faculty of Science and Technology Karnataka Anekal562106 India Kongu Engineering College Department of ECE Erode Tamilnadu Perundurai638 060 India Nandha Engineering College Department of CSE Erode Perundurai638 052 India KSR Institute for Engineering and Technology Tiruchengode Department of CSE 637215 India

ISBN: (纸本)9798350327533

Aim: The purpose of this study is to compare the object detection performance of You Only Look Once V4 (YOLOv4) and Single Shot Multibox Detector (SSD) algorithms with respect to metrics like accuracy and latency. Materials and method: Twenty sample photos in all, from different classifications and labels, were gathered. These samples were divided into training dataset (60 %) and test dataset (40 %). To measure the performance, values for accuracy and latency were computed for YOLOv4 and SSD with G power 0.8. Result: The accuracy in prediction of the object in the image was higher in the YOLOv4 algorithm (97 %) compared to the SSD algorithm (84 %). After running a t-test on an independent sample of the two groups under consideration. It is observed that YOLOv4 reported greater preference than the SSD algorithm having p value 0.166 (p>0.05). It was proven that the YOLOv4 reported greater preference than SSD in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks Machine Learning Novel Custom Dataset Object Detection Single Shot Multibox Detector You Only Look Once V4

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Emotion Recognition Using Bayesian Learning from a Multi-Label Data Corpus

Emotion Recognition Using Bayesian Learning from a Multi-Lab...

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2022 International Conference on Computing, Networking, Telecommunications and engineering sciences Applications, CoNTESA 2022

作者： Swathi, Bandi Kumar, Madapuri Rudra Riyaz Belgaum, Mohammad Alansari, Zainab G. Pulla Reddy Engineering College Computer Science and Engineering Kurnool India G Pullaiah College of Engineering and Technology Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia University of Malaya Computer System and Information Technology Kuala Lumpur Malaysia Information Technology Muscat Oman

ISBN: (纸本)9798350398014

In the digital era of information systems, emotion detection from audio signals is crucial for forensic services and operator or driver emotion monitoring in large-scale companies' safety and security. Speech is a uniquely human trait used to express and communicate one's point of view to others. Emotion recognition from speech audio signals is obtaining a presenter's emotions from the presenter's audio signal. Machine learning is used to develop emotion recognition systems, a critical research goal in current engineering research. The three main stages in speech emotion recognition are extraction of features, feature engineering, and classification. Even though powerful machine learning-based emotion identification algorithms for speech audio signals exist, the detection rate with maximum specificity and sensitivity is not scalable using most modern methods. A significant research objective is feature optimization for emotion recognition from speech audio signals. This article introduced a unique technique called Supervised Bayes Learning on Digital Features (SBL-DF). To scale its performance, the trials compared the suggested approach's performance to an earlier model depicted against a similar goal. Experiment results indicate that the proposed method for optimizing features could be used on a large scale. The reference classifiers' performance in making accurate detections with few false positives. © 2022 IEEE.

关键词： Genetic algorithms

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Comorbidity Diseases Diagnosis Using Machine Learning Methods and Chi-Square Feature Selection Technique 16

Comorbidity Diseases Diagnosis Using Machine Learning Method...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Mukhlif, Dheyauldeen M. Abd, Dhafar Hamed Ejbali, Ridha Alimi, Adel M. Sfax Tunisia University of Anbar College of Computer Science and Information Technology Ramadi Iraq Gabes Tunisia Sfax Tunisia University of Johannesburg Faculty of Engineering and the Built Environment Department of Electrical and Electronic Engineering Science Johannesburg South Africa

ISBN: (纸本)9798350381344

The diagnosis of common diseases, in which people suffer from several bad health conditions, is a complex medical challenge. This study investigated the use of machine learning methods combined with the Chi-square feature selection technique to improve the accuracy and efficiency of comorbidity diseases diagnosis. Using various decision trees of machine learning algorithms, random forest, Gradient Boost, AdaBoost, Bagging, and extra trees, this research aimed to improve the identification and prediction of comorbid conditions and ultimately advance early detection and treatment strategies. Using the selection of Chi-Square features helped give priority to the most relevant attributes, reduce noise, and refine the models. The results showed that both the AdaBoost and Gradient Boost algorithms obtained an accuracy rate of 91.33%, confirming their efficacy in comorbidity diseases diagnosis. However, it is essential to ensure their reliability and efficacy in the real clinical environment, including the practical implementation and validation of these methods, to improve patient care and medical decisions. © 2023 IEEE.

关键词： Adaptive boosting

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A Review on Meta-Learning: How Artificial Intelligence and Machine Learning Can Learn to Adapt Quickly

A Review on Meta-Learning: How Artificial Intelligence and M...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Shrikant Khadse Palash Gourshettiwar Adesh Pawar Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (Deemed to be University) Wardha India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha India Department of Computer Science and Medical Engineering Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamentally changed how Artificial Intelligence (AI) and Machine Learning (ML) are handled, constantly reshaping these fields. Research study reviews the broad field of meta-learning, highlighting its critical function in bridging the AI-ML divide to improve adaptability. The study starts with a review of past developments, following the development of meta-learning algorithms and their underpinning theories. The historical view lays the groundwork for understanding the course of this dynamic regulation, spanning from early efforts in probabilistic program induction to modern discoveries in model-agnostic meta-learning. The study explores multiple meta-learning applications in AI and ML, revealing how they affect financial forecasting, computer vision, natural language processing, autonomous cars, health informatics, robotics, and personalized recommendation systems. When seen from the perspective of adaptive intelligence, meta-learning may be effectively applied to conditions with limited data, hyperparameter optimization, few-shot learning, and quick adaption in reinforcement learning settings.

关键词： Metalearning Adaptation models Renewable energy sources Reviews Shape Reinforcement learning Benchmark testing Regulation Artificial intelligence Recommender systems

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A method of relay UAV deployment for emergency communication scenarios in disaster areas

A method of relay UAV deployment for emergency communication...

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2024 China Automation Congress, CAC 2024

作者： Tao, Cancan Liu, Bowen Han, Yi Chen, Qiangqiang Ye, Jun Zhong, Ronglin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Parallel Robot and Mechatronic System Laboratory of Hebei Province Yanshan University Qinhuangdao066004 China Faculty of Science Kunming University of Science and Technology Kunming650500 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China School of Information Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9798350368604

This article proposes a relay deployment method that uses UAVs to provide communication services to ground users, especially for emergency communication scenarios in disaster ***, a two-layer network communication model is constructed for emergency communication scenarios in disaster areas, and the relay deployment problem is transformed into a multi-objective optimization problem. On this basis, a detailed analysis of the problem is conducted, and a UAV relay deployment method based on the K-means++ algorithm is proposed. The two indicators of the number of UAVs used and the network communication performance are considered in solving the problem. Simulation results show that the proposed method can achieve a balance between the number of relay UAVs used and the achieved network performance. © 2024 IEEE.

关键词： Multiobjective optimization

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Analyzing Textual Data for Mental Health Assessment: Natural Language Processing for Depression and Anxiety 10

Analyzing Textual Data for Mental Health Assessment: Natural...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dixit, Krishna Kant Pundir, Sumit Shrivastava, Anurag Kumar, C Praveen Srivastava, Arun Pratap Singh, Pankaj Gla University Department of Electrical Engineering Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

In this study, depression and anxiety markers found in text data are carefully targeted in order to use Natural Language Processing (NLP) techniques to the evaluation of mental health. The work gives a thorough assessment of NLP models that include sentiment analysis, emotion identification, as well as linguistic pattern detection along with shows significant improvements in F1 scores, recall, and accuracy. The varied dataset, gathered from social media, online discussion boards, and healthcare records, is rigorously preprocessed as well as qualitatively evaluated, enhancing the validity of the method. The findings might advance early identification and intervention by bridging the technical and mental health spheres. Cross-cultural datasets, multi-modal data integration, moral concerns, and cooperation with mental health specialists are important future prospects. © 2023 IEEE.

关键词： Anxiety Depression Emotional Recognition Mental Health Assessment Natural Language Processing Sentiment Analysis

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