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Metarelation2vec:A Metapath-Free Scalable Representation Learning Model for Heterogeneous Networks

Tsinghua science and technology 2024年第2期29卷 553-575页

作者： Lei Chen Yuan Li Yong Lei Xingye Deng School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan 411201China

Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks(HNs)for most of the existing representation learning ***,any metapaths consisting of multiple,simple metarelations must be driven by domain *** sensitive,expensive,and limited metapaths severely reduce the flexibility and scalability of the existing models.A metapath-free,scalable representation learning model,called Metarelation2vec,is proposed for HNs with biased joint learning of all metarelations in a bid to address this ***,a metarelation-aware,biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given ***,grouped nodes by the type,a common and shallow skip-gram model is used to separately learn structural proximity for each node ***,grouped links by the type,a novel and shallow model is used to separately learn the semantic proximity for each link ***,supervised by the cooperation probabilities of all meta-words,the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs,ensuring the accuracy and scalability of the *** experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.

关键词： metarelation random walk heterogeneous network metapath representation learning

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Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images 2

Crafting a Machine Learning Model for Discerning Synthetic a...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Shah, Misha Jaiswal, Gautam Sharma, Dolly Amity School of Engineering and Technology Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331541217

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of 2.7 million images, the model aims for high accuracy in classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Identification of Human Activity from Video Streaming Smartphone Data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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Interpretation of Sign Language Using Machine Learning 2

Interpretation of Sign Language Using Machine Learning

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Srivastava, Palak Hazela, Bramah Singh, Shikha Asthana, Pallavi Pandey, Deependra Singh, Kamlesh Kumar Singh, Vineet Amity University Amity School of Engineering & Technology Department of Computer Science Engineering Uttar Pradesh India Amity University Amity School of Engineering & Technology Department of Electronics & Communication Engineering Uttar Pradesh India

ISBN: (纸本)9798350364699

Sign language detection using machine learning has emerged as a crucial area of research aimed at bridging communication barriers between individuals with hearing impairments and the broader community. This paper explores the application of machine learning techniques for the automatic recognition and interpretation of sign language gestures. A systematic review of existing methodologies, including traditional machine learning approaches and recent advancements in deep learning architectures, reveals key challenges such as variability in signing styles, regional dialects, and real-time processing requirements. To address these challenges, we propose a novel approach that leverages convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which have demonstrated significant improvements in accuracy and generalization across diverse datasets. Our experimental results show the efficacy of these models on benchmark sign language datasets, achieving high levels of accuracy and robustness in real-world conditions. Furthermore, this study discusses the potential societal impacts of sign language detection systems in education, healthcare, and everyday communication, emphasizing the role of technological innovation in fostering inclusivity. By advancing machine learning methodologies, we contribute to the development of accessible communication solutions for individuals with hearing impairments. © 2024 IEEE.

关键词： Adversarial machine learning

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Predictive Analytics in Sports: Using Machine Learning to Forecast Outcomes and Medal Tally Trends at the 2024 Summer Olympics 4

Predictive Analytics in Sports: Using Machine Learning to Fo...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Moolchandani, Jhankar Chole, Vikrant Sahu, Santosh Kumar, Rahul Shukla, Anshita Kumar, Ashok Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper focuses on using machine learning approaches in predicting the medal projections and analyzing the medal distribution pattern in the 2024 Summer Olympics. Due to the availability of a large number of variables both at the individual athlete level and the country level as well as event specific measures, various statistical models are trained to predict the number of medals and unearthing traits that are associated with success in Olympics. The evaluation of the predictions of the models is done using linear regression and random forest along with support vector classifier, as well as artificial neural network classifiers. Using the research findings, it becomes evident that predictive analytics has the capacity of offering a better understanding of the performance of sport teams and formulation of strategies for future competition events. As is observed from the above findings the random forest model sampled the highest accuracy of 99. 034 percent in the general set out of the four models;thus, it is recommended ideal for complicated set data investigations Based on the results of the experiments, it is clearly observed that the random forest has the highest accuracy of 99. 034. © 2024 IEEE.

关键词： Predictive analytics

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Comparative Analysis of IoT and Blockchain Technologies in Enhancing Security and Efficiency in Healthcare Systems 4

Comparative Analysis of IoT and Blockchain Technologies in E...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Kumar, Ashok Shukla, Anshita Thada, Vikas Chole, Vikrant Moolchandani, Jhankar Sahu, Santosh Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper presents an extensive comparison of the IoT and blockchain technologies in the improvement of security, privacy, and interoperability, as well as energy efficiency in healthcare systems. This is because IoT allows for constant tracking and gathering of data from medical devices and this enhances the welfare of the patients as well as the management of the health facilities. However, this has exposed the Internet of Things (loT) technology with multiple risks in data security and privacy since it uses wireless communication and has limited computational power. Such issues can be addressed through the use of blockchain technology because of its distributed and tamper proof ledger. This research work presents a case of how the integration of IoT with blockchain will lead to a middle of the two worlds, where IoT will be useful for handling real time data while blockchain will handle the issue of security. The study concludes that the integration of blockchains with the IoT offers superior results than the individual implementations in several aspects, thus offering a more secure, efficient, and interoperable environment for today's healthcare applications. Possible future work includes improving the integration of these technologies to minimize the energy consumption, improve the scalability of the system and establish feasible implementation models in actual health care systems. © 2024 IEEE.

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Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

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International Journal of computers and Applications 2024年第7期46卷 496-507页

作者： Inje, Bhushan Nagwanshi, Kapil Kumar Rambola, Radhakrishna Amity School of Engineering and Technology Amity University Jaipur India NMIMS University MPSTME Shirpur Shirpur Dist Dhule India Department of Computer Science and Engineering SoS Engineering and Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.4

关键词： Clustering algorithms

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Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure Recognition: A 1D-CNN LSTM Approach 11

Bayesian-Enhanced EEG Signal Analysis for Epileptic Seizure ...

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

作者： Jain, Mantra Bhardwaj, Harshit Srivastav, Ansh Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

The intricate neurological condition known as epilepsy, which is common across the world, presents consid-erable difficulties in accurately identifying and differentiating between non-epileptic and epileptic activity using electroen-cephalograms (EEGs). To customize successful therapies, it is essential to accurately identify the kinds of epileptic activity. Since epilepsy affects about 50 million people worldwide according to latest update of WHO and is typified by spontaneous seizures, early identification and prediction are vital in enabling people to minimize possible harm. The current seizure prediction methods lacks, with particular emphasis on their limited performance on small training datasets and their disregard for time-series data. To identify small patterns in EEG data, this study combines cutting edge technologies, like Long Short-Term Memory (LSTM) and 1D-CNN (Convolutional Neural Network) leveraging data from five hundred patients acquired from the UCI Machine Learning Repository. To optimize the 1D-CNN LSTM architec-ture and hyper-parameters, Bayesian optimization is employed, allowing for efficient exploration of the parameter space. It's effectiveness is not only limited to enhancing the performance metrics of a particular model but also minimizing the computing power required for fine tuning. The research evaluates the effectiveness of the 1D-CNN LSTM-based model, showcasing its potential as a reliable tool for automated epilepsy detection with accuracy of 99.47% (100%), average sensitivity of 99.45%, and average specificity of 99.57%. This approach, emphasizes the significance of anticipating seizures in advance, attempts to provide epileptics the tools they need to control and avoid seizures in advance, so ultimately enhancing their quality of life for patients. © 2024 IEEE.

关键词： Quality control

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Predicting Student Academic Performance using Machine Learning: Analyzing Socio-Economic and Personal Factors from Secondary Education in Portugal 4

Predicting Student Academic Performance using Machine Learni...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sahu, Santosh Kumar, Ashok Kumar, Rahul Shukla, Anshita Chole, Vikrant Moolchandani, Jhankar Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798350387490

This paper aims to predict student academic performance using machine learning algorithms by analyzing socioeconomic and personal factors from secondary education in Portugal. The study utilizes a dataset containing demographics, family background, educational support, extra-curricular activities, and academic records to evaluate their impact on students' success in Mathematics and Portuguese courses. Machine learning models such as Logistic Regression, Decision Trees, and Random Forests were employed to analyze the data. The Random Forest model outperformed others with an accuracy of 85 %, indicating significant potential in predicting student performance using the selected features. © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-Based Object Detection Model Using Amazon Rekognition 1

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Singh, Darshita Arora, Deepak Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh India

ISBN: (数字)9789819998111

ISBN: (纸本)9789819998104

Object identification is a well-known research subject in the field of computer vision, with various applications like surveillance, autonomous driving, and robotics. The integration of machine learning with cloud computing has enabled organizations to automate many procedures and tasks, cut costs, and boost efficiency. With the help of a wide range of machine learning (ML) services offered by cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), organizations may take advantage of ML’s potential without the need for specialized equipment or costly staff. A cloud-based ML service called Amazon Rekognition offered by Amazon Web Services is a powerful tool for object identification. Through this paper, the authors offer a study on the application of Amazon Rekognition for object detection and recognition. The idea is to detect objects in the provided images using machine learning and deep learning algorithms provided by Amazon Rekognition. The effectiveness of Amazon Rekognition in recognizing objects in images is precisely examined by the authors, who compare the discovered objects with state-of-the-art object detection algorithms and then provide the result with a corresponding confidence percentage. Experimental results show that Amazon Rekognition handles object detection tasks well, achieving a good balance between accuracy and speed. It is an effective tool for object detection with high average precision and recall values for many object categories. However, accuracy may vary depending on the complexity of the objects in the image, the lighting conditions, and other factors. Amazon Rekognition is a managed service that makes use of encryption, access control, compliance, monitoring, and logging. While the infrastructure and security are handled by AWS, it’s crucial to incorporate security best practices within the application for maximum security. It is important for developers to carefully evaluate the perf

关键词： Object detection

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