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共 70853 条记录，以下是251-260 订阅

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In time recommendations through an Associative Classifier and LookBackApriori: a case study

In time recommendations through an Associative Classifier an...

引用

Workshops of the EDBT/ICDT 2024 Joint Conference, EDBT/ICDT-WS 2024

作者： Dalla Vecchia, Anna Marastoni, Niccolò Quintarelli, Elisa Dept. of Computer Science University of Verona Verona Italy

Recommender systems are becoming essential tools in many scenarios, as they help users extract hidden knowledge and useful insights from datasets. In many real domains, the temporal order between events, combined with their contextualization, improves the accuracy of provided suggestions. In this paper, we introduce a framework designed to mine personalized, in time, contextual, and explainable sequential rules useful to provide recommendations for a predefined target parameter. Specifically, this framework is composed of the L3 Associative Classifier and LookBackApriori, a modification of Apriori algorithm. Our proposal takes historical data and contextual information as input and generates two sets of rules: the first set comprises rules that allow enhancement of the target parameter, and the second makes it worse. The proposed technique is applied to a real-world scenario involving data collected by Fitbit wearable devices to improve the user's sleep score after performing fitness activities in different contexts. The idea has been evaluated on two real datasets, and the results confirm the positive effects of the combination of L3 with LookBackApriori. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Data mining

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FashionAI: Image-Based Clothing Detection and Shopping Recommendation 3

FashionAI: Image-Based Clothing Detection and Shopping Recom...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Jain, Disha Thazhathu, Elizabeth Maria Adiraju, Isha Bhattacharya, Juhi Singh, Dinesh Pes University Dept. of Computer Science Bengaluru India

ISBN: (纸本)9798350381931

In today's world, the retail fashion industry is going through radical transformations in terms of technology. The magnitude of options available on the internet makes it difficult to find suitable clothing items on online retail stores in an effective manner. To overcome this, we aimed to create a project that detects specific items of clothing from an image and makes real-time recommendations. We have developed a system which uses computer vision techniques to analyze images and recognize different types of clothing such as shirts, pants, dresses, etc. The system will then use machine learning algorithms to make product recommendations based on the similarity between the image uploaded by the user and products available on various e-commerce websites. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Feature Descriptors for Human Interaction Recognition in Outdoor Environments

Multi-Feature Descriptors for Human Interaction Recognition ...

引用

2024 International Conference on Engineering and Computing, ICECT 2024

作者： Kamal, Saleha Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798350349719

In recent years, Artificial intelligence has gained significant attention in automatically recognizing human interaction. Thus, leading researchers to develop efficient Human Interaction Recognition (HIR) systems. Efficient feature extraction is the key to developing a system that achieves higher accuracies. Feature extraction emphasizes identifying robust features that can effectively distinguish various human behaviors. This paper introduces a novel approach enabling machines to understand human behaviors in an outdoor environment to recognize human interactions efficiently. This research aims to extract key body points from human silhouettes for robust spatio-Temporal features that exhibit unique characteristics for each interaction. Our proposed human interaction recognition (HIR) system recognizes six complex human interactions taken from UT-Interaction dataset i.e. handshake, punch, kick, point, hug, and push. We have designed hybrid feature algorithms and a multiclass support vector machine (SVM) for interaction recognition. To evaluate our system's performance, we have computed Mean recognition accuracy and compared it with other state-of-The-Art classifiers. The experimental results showcase the reliability of our proposed approach in handling complex real-world scenarios. Thus, making it applicable to vast domains such as smart homes, violence detection, security systems, and surveillance. © 2024 IEEE.

关键词： Support vector machines

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Deep Learning Model for Detecting the Presence of Neurological Conditions in MRI and CT Scans 2

Deep Learning Model for Detecting the Presence of Neurologic...

引用

2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable Development, ICRAIS 2024

作者： Kanchan, Jayapadmini Ananya, H.N. Anoksha, N.A. Hegde, Sarika Dept. of Computer Science and Engineering Nitte Karkala India

ISBN: (纸本)9798350354461

An AI-powered MRI/CT analysis solution for medical professionals can significantly enhance diagnostic accuracy and efficiency. By harnessing deep learning algorithms, this solution can minimize human error and accelerate image interpretation. The incorporation of a Convolutional Neural Network (CNN) model empowers the solution to identify abnormalities in MRI/CT images with high consistency and reliability. This solution supports intricate case evaluations and integrates effortlessly with electronic health records, making high-quality diagnostic capabilities available to a wider range of healthcare professionals. The solution is meticulously designed to detect various abnormalities, including tumors, Alzheimer's, and hemorrhages from MRI images, as well as COVID-19 and checking for squamous conditions from CT images. Ultimately, this solution aims to improve patient outcomes through precise and prompt diagnoses, while also serving as an educational resource and contributing to ongoing medical research and innovation. The brain tumor MRI model achieved an accuracy of 87.5% after 100 epochs, while the chest CT scan model attained 95.65% accuracy in just 50 epochs. Brain hemorrhage MRI model accomplished 88.45% accuracy in 30 epochs, and the COVID-19 dataset model reached 78.9% accuracy in 40 epochs. Lastly, the Alzheimer's model obtained an accuracy of 80.70% after 40 epochs. © 2024 IEEE.

关键词： Convolutional neural networks

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Share-a-Meal: A Mobile App-based Food Sharing System 6

Share-a-Meal: A Mobile App-based Food Sharing System

引用

6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Uddin, Mohammad Salah Ansary, Fozlay Rabby Dept. of Computer Science and Engineering East West University Bangladesh

ISBN: (纸本)9798331522667

Many problems have been solved using the internet of things (IoT). Waste management, resource management, traffic management, financial services (e.g. stock rate prediction), logistics services, agriculture, etc. are the major sectors where IoT has played a fruitful role. The use of IoT is increasing day by day due to its immense advantages. This paper presented an IoT-based system for sharing leftover food with others or hungry people. The system consists of a mobile application and an embedded system. Cloud service is used as middle-ware between the mobile application and embedded system. Mobile application is used for sending and receiving notifications. The rectangular-shaped box type embedded system is responsible for food collecting and distribution. The development and prototype implementation of our proposed system are discussed throughout this paper. The system helps to share leftovers (food) with others which can reduce food wastage. © 2025 IEEE.

关键词： Cloud-Food Food waste Food-sharing internet of things Leftover food

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Understanding the Evolution in Tourist Behavior Patterns through Context-Aware Spatio-Temporal k-Means

Understanding the Evolution in Tourist Behavior Patterns thr...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Belussi, Alberto Vecchia, Anna Dalla Gambini, Mauro Migliorini, Sara Quintarelli, Elisa University of Verona Dept. of Computer Science Verona Italy

ISBN: (纸本)9798350362480

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022. © 2024 IEEE.

关键词： Spatio-temporal data

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Computational Approach for Halegannada to Hosagannada Poem Translation 4

Computational Approach for Halegannada to Hosagannada Poem T...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Harsha, A.C. Baig, Toukheer Gurav, Laxmikant Bhujang Bhat, Mahesh Shripad Narayan, Surabhi Pes University Dept. of Computer Science Bangalore India

ISBN: (纸本)9798350349900

Halegannada, or Old Kannada, occupies a pivotal place in Karnataka's linguistic and cultural tapestry and cultural heritage, embodying a significant epoch in the evolutionary timeline of the Kannada language, with a history spanning over 2000 years. This research pioneers the translation of Halegannada poetry into contemporary Hosagannada, employing cutting-edge Natural Language Processing (NLP) models. Indic languages exhibit a notable scarcity of substantial parallel and monolingual corpora in comparison to English and other European languages. Dravidian languages requires more annotated data compared to European or Indo-European languages. Within the realm of Indic languages, several Dravidian languages are categorized as low-resource languages and Halegannada is one among them. To overcome this limitation, strategies such as Back Translation, Unsupervised Machine Translation, Neural Machine Translation, Unsupervised Neural Machine Translation (UNMT), and Transfer Learning are employed. To overcome the lack of pre processed Halegannada data, a comprehensive dataset comprising 20,000 meanings extracted from Pampa's architectural work is created. This paper proposes a Halegannada word splitting model based on LSTM and a computational approach for Halegannada to Hosagannada poem translation. The word splitting model has an accuracy of77.92%. © 2024 IEEE.

关键词： Neural machine translation

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Robust Object Recognition with Genetic Algorithm and Composite Saliency Map 5

Robust Object Recognition with Genetic Algorithm and Composi...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Object detection and recognition have emerged as crucial research areas, driven by the dire need to address the challenges posed by identifying and recognizing objects in diverse domains. Accurate object identification and recognition is required for tasks such as autonomous navigation, video surveillance, precision agriculture, and medical imaging. We propose a method based on a unique combination of machine learning techniques for object recognition. The proposed approach starts by applying K-means segmentation to the input image to cluster similar regions and colors. Next, a composite saliency map is generated based on the K-means segmentation output. Subsequently, the technique of extracting objects from the saliency map is accomplished through the employment of the connected pixel object extraction method. Ultimately, the Genetic Algorithm is utilized to optimize decision tree classifier for the purpose of object recognition. The MSRC dataset was utilized to assess the proposed approach, resulting in an accuracy of 81.5%, accompanied by a precision of 83.3% and recall of 86.1%. The results depicts that the our approach is highly valuable for accurate object categorization, wherein the utilization of Genetic Algorithm significantly enhances the precision of the classification. © 2024 IEEE.

关键词： Genetic algorithms

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Drone-based Human Surveillance using YOLOv5 and Multi-Features

Drone-based Human Surveillance using YOLOv5 and Multi-Featur...

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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Zahoor, Laiba Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331510503

Drone use has expanded recently to include airborne photography, human action recognition (HAR), search and rescue (SAR), and surveillance. Drones have a distinct edge in human action recognition (HAR) due to their bird's-eye vision, but this also brings challenges such as shifting backgrounds and obscured perspectives, which call for creative solutions. To address these issues, we propose a method that processes video frames using preprocessing techniques, detects human presence using YOLO, and human silhouettes are extracted using morphological erosion. We focus on key skeletal landmarks such as the head, wrists, ankles, and neck to analyze human movement patterns. We enhance feature extraction with methods like MSER and HOG. Classification is performed using Naïve Bayes algorithm, with feature optimization achieved through the Grey Wolf optimizer. Our method is tested on the Drone-Action dataset and shows itself to be rather effective, detecting human activity with an astounding 84% accuracy. © 2024 IEEE.

关键词： Drones

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Robust Machine Learning Approach to Plant Species Classification for Sustainable Agriculture 3

Robust Machine Learning Approach to Plant Species Classifica...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Sustaining resilient agricultural practices is essential for the overall yield because it guarantees both economic stability and food supplies. By incorporating Zero-Shot learning into a strong leaf classification architecture, our method meets this demand. We optimized an Al-powered four-step categorization process using the Bee Colony Algorithm. To pre-process, segment, extract features from, and classify leaf image data, zero-shot learning is employed. Pre-processing, which includes histogram equalization and contrast stretching, is followed by morphological segmentation. In feature extraction, two methods are used: Harris corner detection and SURF (Speeded Up Robust Features). Zero-Shot learning for classification is optimized using the Bee Colony Algorithm. At 92.73% accuracy, the suggested method surpasses out previous methods like CNN and SVM, suggesting that it has the potential to enhance crop quality and disease control in agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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