Reviewerly/exHarmony huggingface dataset:
Construction of a Three-dimensional Geological and Mineral Resource Prediction Model Based on Machine Learning Algorithms

In the current environment of rapid development of “big data”, the basic idea of integrating and exploring multiple types of information represented by “multi source heterogeneous” geoscience big data is consistent with the idea of “big data”. Based on the decision tree prediction model, this paper used the random forest algorithm to upgrade five mineral models. First, the machine learning mineral resource prediction was discussed, then the geological model was established using three-dimensional technology, then the three-dimensional quantitative prediction was carried out, and finally the relevant experimental analysis was carried out. The experimental results show that when using the random forest algorithm, the model accuracy can reach up to 95.8%. It was found that the random forest model had higher prediction accuracy and shorter prediction time. The research in this article was expected to provide new ideas and methods for predicting and evaluating the spatial distribution patterns of mineral resources in the future.
Almost Pilotless Channel Estimation Using Dimension Reduction by PCA

In this paper, an almost pilotless channel estimator (APCE) using machine learning based on Principle Components Analysis (PCA) is proposed. The APCE extracts PCA vectors from a wireless communication channel and estimates the phase and amplitude of the channel using an ML model trained on PCA coefficients. Through the highly accurate phase estimation using the ML and the amplitude estimation process that suppresses noise, the APCE obtains an signal-to-noise ratio (SNR) gain of 6 to 7dB compared to the least-squared channel estimator in the SNR region where QPSK operates. Research results that can reduce the computational complexity that inevitably occurs while using ML are also provided at the end of the paper.
Image Caption Generation using ResNET-50 and LSTM

Generating image captions presents a formidable challenge, requiring a deep understanding of image content to produce coherent and descriptive textual descriptions. This research introduces an innovative approach for image caption generation, leveraging the ResNet-50 convolutional neural network (CNN) and the Long Short-Term Memory (LSTM) recurrent neural network. The ResNet-50 CNN serves as a robust feature extractor, encoding essential visual information from the input image. Through fine-tuning on a comprehensive dataset, the pre-trained ResNet-50 model becomes proficient at capturing the semantic essence of images. We employ an LSTM network as a language model to craft textual descriptions. It inputs the encoded ResNet-50 features and progressively generates captions word by word. Our LSTM model is trained on an extensive collection of image-caption pairs, enabling it to grasp the intricate connections between visual attributes and corresponding text descriptions. Empirical results unequivocally demonstrate that our proposed model surpasses existing methods in terms of caption quality and semantic coherence.
Critical Analysis of BERT and LSTM Model for Bengali Sentiment Analysis Across Varied Datasets

Bengali sentiment analysis is a fast-developing field with a wide range of uses, including social media monitoring, market research, and user feedback analysis. In order to develop reliable models that can accurately represent the complex emotional expressions found in Bengali text, this study explores Bengali sentiment analysis. This research stands out for its thorough examination of multiple sentiment analysis models, including both conventional BiLSTM models and cutting-edge BERT-based models. Our analysis provides insightful information about the performance of the model on three different sentiment datasets: SentiNob, Social Media Comments, and Bengali Sentiment. The best-performing models highlight differences between datasets and highlight the impact of dataset-specific features. Kowsher/model-bangla-bert is a highly adaptive performer in the SENTINOB dataset, demonstrating adaptability with a strong F1 score of 73% and Accuracy of 72%. In close pursuit, Csebuetnlp/banglabert exhibits dependability and equilibrium, achieving the highest F1 score and accuracy of 75% and 74%, respectively. With an amazing F1 score of 90%, Kowsher/model-bangla-bert emerges as the top performer in the SOCIAL MEDIA COMMENTS dataset, demonstrating remarkable precision and recall. Kowsher/model-bangla-bert is a standout performer in the BENGALI SENTIMENT dataset, exhibiting adaptability while sustaining a high F1 score of 67% and the highest Precision of 70%. We find that FastText + BiLSTM consistently achieves a competitive F1 score of 65%. By providing a thorough comparative analysis of these models across several datasets, this study not only closes a significant gap in the literature but also provides a roadmap for future research in Bengali sentiment analysis. We suggest future directions for domain adaptation, multi-class learning, and improvement. This research lays the groundwork for the creation of more responsible and effective AI models in the field of sentiment analysis in Bengali by carefully examining various models, datasets, and performance metrics.
Optimized photovoltaic power forecast using k-means clustering based error reduction

Due to the increasing number of renewable energy sources (RES) in Germany such as photovoltaic (PV) systems and the resulting risk of overloading the power system, consumption and renewable generation must be coordinated locally. Therefore, it is necessary to predict the power generation from individual PV systems locally, which is challenging because of the volatile generation. This paper presents a method for the determination of an error correction coefficient (ECC) for the reduction of the forecast error of a PV power forecast. The ECC is calculated using the method of k-means clustering from the predicted and the measured PV power. The PV power forecast for the day-ahead prediction of the generated PV power is based on an artificial neural network (ANN). The ANN is trained with historical weather data and predicts solar irradiance using a numerical weather prediction (NWP). The results of a test with a real PV system show that the error of the PV power forecast can be reduced by applying an ECC. In this work, the error is reduced from 5.75% to 4.42% on average.
Research on Personalized Recommendation Algorithm for News on New Media Platforms

In the era of new media, the progress of communication technology has driven the development and transformation of news formats. Algorithm recommendation news can provide tailored content to meet the personalized needs of the audience and stimulate their initiative. This article proposes a news recommendation algorithm based on user interest and news popularity. By using the BERT model to transform the extracted news keywords into directly computable word vectors, the Euclidean distance between each word vector is calculated and clustered to obtain several interest center vectors for users. We propose a content similarity calculation formula that combines user interest vectors and word vectors extracted from candidate news to measure the similarity between user interest and candidate news. We conducted the algorithm experiment on the platform of Today's Headlines, and the experimental results showed that its accuracy, precision, recall, and F1 score were higher than those of the comparative algorithms.
Text-to-Image Conversion using Deep Learning

This study addresses the field of text-to-image conversion using deep learning techniques. The problem at issue concerns producing lifelike images from written descriptions, which has implications for design, visual storytelling, and content development applications. To tackle this problem, this paper presents a thorough strategy that makes use of the features of Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs). The process involves transforming written descriptions into vectors of a defined length and using a generator network to generate visuals that correspond to those vectors. A discriminator network is utilized to evaluate the authenticity of these images in terms of fidelity. To improve alignment between the text and the generated visuals, a text-image matching loss is incorporated into the model's iterative training method. The results of this study show a notable advancement over current methods for text-to-image synthesis. Strict testing and quantitative measures like Frechet Inception Distance and Inception Score are used to validate this breakthrough. In addition to its immediate uses, the research could have revolutionary effects on accessibility, design, producing content, and interaction between humans and computers by making it easier to create visuals from textual data.
Secure Federated Learning-Based Privacy Preserving in V2V Communications

Vehicular Ad-Hoc Networks (VANETs) have emerged as a promising technology to improve road safety, traffic efficiency, and provide various intelligent transportation services. However, the widespread deployment of VANETs raises concerns about data privacy and security, as it contains sensitive data about vehicles and their drivers. Traditional approaches for sharing and analysing VANET data centrally could compromise individual privacy and expose users to various security risks such as position falsification, man-in-the middle attacks and data poisoning attacks. To address these privacy challenges, a secure Federated Learning (FL)-based privacy preserving in V2V communications is proposed. FL is a decentralized machine learning that allows multiple vehicles to collectively build a global model while storing the data on their devices. It facilitates model training without sharing raw data, thereby mitigating privacy risks and ensuring confidentiality. To ensure data integrity and security during the FL process, cryptographic technique is incorporated to detect and mitigate the model poisoning and data poisoning attacks. By leveraging cryptographic techniques, and applying the enhanced federated averaging, the proposed scheme attains improved performance in terms of accuracy, precision and recall over the existing models while promoting collaboration and knowledge sharing in intelligent vehicular networks.
Aspect-Level Sentiment Analysis Review: Recent Advances and Trends

Aspect-level text sentiment analysis is a fine-grained approach aimed at delving deep into the emotional content embedded within text, providing a more comprehensive understanding of users' attitudes and opinions towards various aspects of entities. This paper establishes a solid foundation for research by conducting a comprehensive study of sentiment analysis-related literature from the past 15 years, with searches conducted in both the CNKI and Web of Science core databases. The paper introduces multiple levels of text sentiment analysis and their research methodologies. Regarding aspect-level text sentiment analysis, we conduct an in-depth analysis from various perspectives, summarizing widely used methods and models, as well as applicable contextual scenarios. Finally, this paper discusses the challenges faced by aspect-level text sentiment analysis and offers insights into its future development trends.
Concepts, Approaches, and Challenges in a Study of Sentiment Analysis

Sentiment analysis (SA) describes the process of carefully going over content that has been saved online to identify and categorize the viewpoints expressed in a text. This process is to assess the author's point of view about a certain object, movie, subject, etc. Positive, negative, or neutral outcomes are possible. These studies provided examples of several SA strategies for extracting and analysing feelings related to the topic's polarity of positive, negative, or neutral. Social media networks SA may be a good resource for data and information. The relationship between big data methods and their use in the field of SA is also briefly discussed. There has been a notable advancement in the usage of Big Data (Hadoop) in the process of gathering information and evaluations from social networks for analysis in recent times.
Comprehensive Evaluation of English Learning Apps Based on Natural Language Processing

Mobile app-based learning is considered a futureoriented educational paradigm and an indispensable mode of learning in the future. Objective and effective evaluation of English learning apps holds significant importance in enhancing teaching effectiveness. The main work of this paper includes the following content. It elucidates the understanding of natural language processing technology, outlines the principles for constructing evaluation criteria for English learning apps, and extensively explores both rational and emotional aspects of evaluation criteria for English learning apps. Finally, it analyzes the developmental trends of natural language processing technology in the comprehensive evaluation of English learning apps. The primary aim of this work is to promote education in a more rational direction by offering a comprehensive set of evaluation criteria for English learning apps, which can be utilized by third-party assessment agencies or individuals. This facilitates a more efficient selection of suitable learning software for end-users and provides valuable recommendations for software developers to enhance their products.
Subject-Verb Agreement Error Handling using NLP: A Literature Review

Subject-Verb Agreement errors in English language are a common problem in Natural Language Processing. These errors occur when the Subject and Verb in a sentence do not agree in number or person, leading to a grammatically incorrect sentence. Over the past few decades, researchers have developed various approaches to automatically detect and correct these errors in text. In this paper, a comprehensive review has been provided of the existing literature on Subject-Verb Agreement error detection and correction in Natural Language Processing. This review study focuses on key techniques that are used in Rule-based, Machine learning, and Deep learning approaches to detect and correct the errors. The performance of the approaches is compared using various evaluation metrics, such as Accuracy, Precision, Recall, and F1 score on standard benchmark datasets. Additionally, the limitations and challenges of each approach are discussed, and potential directions for future research are suggested. This review will be helpful for researchers and practitioners in the field of Natural Language Processing, particularly those interested in Grammar error detection and correction in the English language.
An Evaluation of Quality Metrics for Neural Radiance Field

As Neural Radiance Field (NeRF) models unfold, the evaluation of perceptual quality becomes paramount for ensuring good quality of experience for users. This is especially important in new services like VR/AR and e-learning. In this paper, we evaluate the performance of image and video quality metrics on NeRF-synthesized contents. With a keen focus on correlating a NeRF model with human perception, we conduct a subjective study by gathering crowd opinions scores which are used as the ground truth for evaluation. The results show that deep learning based models like DISTS and CompressVQA provide the best metrics, while conventional metrics like PSNR, SSIM, and MS-SSIM have rather low performances.
Blind Image Quality Assessment With Multimodal Prompt Learning

Assessing the perception of visual content has long been a challenge in the field of computer vision. Numerous mathematical models have been created for image quality assessment (IQA). Despite the efficacy of these tools in quantifying visible distortions, their connection to human perception remains somewhat indirect. Particularly in capturing more abstract perception of visual content without any reference information (or blind image quality assessment), existing methods often rely on supervised learning with labeled data obtained through labor-intensive user studies. Recent research has deviated from traditional approaches by delving into the rich visual language embedded in Contrastive Language- Image Pre-training (CLIP) models. These studies aim to evaluate the quality perception of images without the need for explicit task-specific training. In a similar vein, this work introduces a new IQA model based on multimodal prompt learning (denoted MaPLe-IQA), for blind image quality assessment. Extensive experiments have been conducted on controlled IQA benchmarks. Findings demonstrate that the proposed quality model is capable of capturing perceptual assessments effectively without relying on any reference images. To our best knowledge, this is the first study that adopts the pretrained MaPLe backbone for the IQA tasks. The code for implementing this model is publicly accessible at https://github.com/luutn2002/mapleiqa.
Image Colorization Enhancement Techniques using Deep Convolutional GANs

Image colorization, which refers to adding color to a grayscale or black-and-white image, is a popular subject in image processing. Usually, it's done by hand using colored pencils, crayons, and paints. It may also be done digitally using a variety of software programs like Adobe Photoshop. The field of image colorization has been greatly impacted by the introduction of AI and deep learning algorithms in image processing. This has reduced the time and effort needed to perform the task manually by enabling automatic colorization of grayscale images with a higher degree of accuracy. Numerous neural networks and deep learning techniques have been applied to this subject; the most widely used is the CNN, which was created especially for processing and identifying images. Moreover, a different kind of deep learning model called a GAN is becoming more and more well-liked for the aforementioned purpose, improving the effectiveness and performance of the standalone neural network models to produce an even better result. The proposed study compares the effectiveness of the CNN model in image processing and colorization with the Deep Convolutional GAN model using a variety of assessment measures such as PSNR, SSIM, SCC, and RASE. It also shows how the latter approach appears to be more appropriate than standalone CNN for the mentioned task. Thus, DCGAN turns out to be a better solution for image colorization because it reduces the need for human labor, enhances colorization results, and retains the structural integrity of the image
Developing Email Software Quality Using Software product Concepts

Software quality refers to zero free error product after releasing to the market as possible. This paper is an attempt to determine the effect of email software product errors and mistakes that are well exploited by an attacker. It is involving answers of questions for instance, how email products could be mature in the newer versions, how email software products could be more protected of the cyber-attacks, how the software product concepts might be used to prevent the software exposure of attacks. This study is attempting to building relationships between product concepts to minimize the effect of cyber-attacks and make the future product versions free of errors as possible.
Automatic Recognition Algorithm for Wireless Network Communication Joint Modulation Based on Blockchain Technology

With the development of digital equipment applications related to communication modulators, research on amplitude and phase optimization of digital signals and calibration of acquisition and processing has become increasingly important. In the entire wireless network communication joint modulation automatic recognition, how to optimize the system recognition accuracy and efficiency is currently a key issue that needs to be urgently solved. This article analyzes the current situation of wireless communication joint modulation, utilizes the updated membership matrix and the calculation formula for assigning signal sample points to the feature set, and combines the designed automatic recognition algorithm to construct an improved system. The following conclusion is drawn: the average recognition accuracy of the system on the six selected modulation signal samples is improved by about <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$11.8{{\% }}$</tex> , and the average recognition efficiency of the system is improved by about 9.4%. This indicates that the wireless network communication joint modulation automatic recognition algorithm based on blockchain technology has good practical application effects. This research aims to improve the efficiency and reliability of communication systems, simplify system configuration and management, and promote the development of multimodal communication.
Implementation of Power Grid Fault Diagnosis and Prediction Platform Based on Deep Learning Algorithms

With the development of the power system, the issue of power grid operation safety is receiving more and more attention, deep learning algorithms can effectively solve the above problems. This article uses deep learning algorithms to preprocess, train, analyze fault data from different regions. By building visual technology based on deep classification models to achieve real-time diagnosis of power grid faults, this article designs the basic structure of the prediction platform. Afterwards, the performance of the platform was tested, the system test results showed that the feature extraction efficiency was over 80%, the recognition rate was over 90%, the visualization efficiency was over 91%, the system prediction time was 1-2 seconds. The network fault diagnosis and prediction platform requires a large amount of network data to train prediction deep learning algorithms. Research has shown that the accuracy of data quality is crucial for the effectiveness of the platform. Therefore, preprocessing steps are required to obtain a reliable and representative dataset. This article uses representation engineering domain knowledge technology to select features that have a significant impact on fault diagnosis prediction, and introduces them into deep learning models for prediction.
LVSUM-Optimized Live 360 Degree Video Streaming in Unicast and Multicast Over Mobile Networks

By allowing users to adjust their field of view (FoV) in real-time, live 360° video streaming provides an immersive experience. Recent research focuses on bitrate adaptation under limited resources for 360° video streaming with temporal and spatial viewport adaptation. In this study, we focus on proposing an estimate of Live 360 Degree Video Streaming in Unicast and Multicast over Mobile Networks that optimizes bandwidth allocation to user groups to enhance their experience while taking advantage of the inherent diversity in network conditions and users' field 0 fv iew (FoV). T he proposed method - L VSUM-is proven to improve FoV bitrates from 10.05% to 23.04% in comparison with the recently referenced approaches.