Reviewerly/exHarmony huggingface dataset:
Uncovering the Hidden Patterns: A Novel Approach to Anomaly Detection with Machine Learning

Anomaly detection has a distinct level of issue complexity than the vast majority of analytical and learning issues and activities.This is due to the singular nature of the phenomenon being investigated.The aforementioned inherent obstacles and unresolved identification concerns in complex anomaly data are compiled in this paper.It has come to the attention of those working in data mining, machine learning, computer vision and statistics that the process of anomaly detection is becoming an increasingly important one.This is a result of the increasing demand for the application of these technologies across a wide range of sectors, including risk management, security, compliance, financial monitoring, and AI safety.Deep learning has lately pushed the bounds of various different learning tasks due to its incredible ability to learn expressive representations of complicated data, such as high-dimensional data, temporal data, geographical data, and graph data.Deep learning for anomaly detection, also known as deep ADE, is an application of deep learning that makes use of neural networks to learn feature representations or anomaly scores in order to identify anomalies.When it comes to resolving difficult detection problems in a range of real-world circumstances, several deep anomaly detection algorithms have been developed, and they have demonstrated to perform noticeably better than standard anomaly detection techniques.This article will attempt to provide an all-encompassing analysis of the subject matter covered here.
Exploring the Transfer Learning: A Comparative Study of Pre-trained Models and Fine-tuning Techniques on image dataset

In the modern world, RAM for computers is affordable and widely accessible.For a small investment or rent, we can get the hundreds of GBs of RAM we need to run a super complex supervised machine learning problem.However, getting access to GPUs is not free.We need access to GPUs with 100 GB of VRAM, but getting there won't be easy and will cost a lot of money.Now, in the future, that might change.However, for the time being, it means that we must use our resources more wisely when tackling Deep Learning issues.Particularly when we attempt to address challenging real-world issues in fields like speech and picture recognition.Once your model has a few hidden layers, adding more would require an enormous amount of resources.When the high-level attributes that the lower layers have learnt are insufficient to distinguish the classes in your issue, transfer learning will not be effective.For instance, a pre-trained model could be excellent at recognizing doors but not at determining whether they are open or closed.Instead of high-level features in this situation, utilize the low-level features from the pre-trained network.In this situation, you will either need to leverage features from older levels or retrain additional model layers.Features transfer poorly across datasets that are not comparable.In further depth, this research examines dataset similarity.Nevertheless, the paper demonstrates that using pre-trained weights as the network's initialization improves performance over using random weights.In such a pre-trained event, transfer learning is unlikely to be effective.Because removing layers decreases the number of trainable parameters, overfitting may occur.Furthermore, it takes a lot of time and effort to figure out how many layers to remove without overfitting.
Association Rule Mining in Transactional Data: Challenges and Opportunities

Due to the rapid emergence and evolution of e-Commerce, many retailers are now looking for new ways to improve their customer's purchasing behavior.One of these is through the use of Market Basket Analysis (MBA), a data mining tool that allows them to identify hidden relationships among various products.This method can help them make informed decisions when it comes to marketing and sales.The use of association rule mining (ARM) is a powerful technique that can help retailers identify and analyze the relationships among various products in their large transaction databases.With the help of Market Basket Analysis, they can then devise effective marketing and sales strategies.The goal of this study is to analyze the buying behavior of consumers in an e-Commerce store using ARM.It will then use the collected data to improve the marketing strategies of the store.Through the use of FP Growth and Apriori, we were able to create association rules that are based on the data.The findings of this study provide a deeper understanding of the buying habits of consumers in e-Commerce stores.They also offer practical recommendations for optimizing the sales process and product placement.
Embedding of word vectors with emotion and semantic information by semi-supervised deep learning method: enhancement of sentiment analysis

Maximum all the information learning methods are work on text data sets for that they collect word vector and ignore the emotic information.In todays word emotion and feelings are as important as words.So, to improve the sentiment dataset categorization, herein research we use deep neural network method for embedding the emotion and word vector together and then perform the sentiment analysis.And shows the improvement in the text or word categorization ability and furthermore efficiently elucidate the difficulty of lengthy text emotion categorization sample selection in semi-supervised learning technique.In this paper, active learning and adaptive deep confidence network are combined to construct a new semi-supervised learning method-active adaptive deep belief network (ADN), and it is successfully applied to sentiment classification tasks.
Reinforcement Learning in Real-World Environments: Challenges and Opportunities

Due to their appealing qualities in power quality and ease of expansion compared to two-level or three-level inverters, cascaded H-bridge (CHB) inverters have been widely used in distributed photovoltaic (PV) power generating systems.Because they can automatically balance the power difference between phases, modular inverters with an interphase structure made up of four-port isolated DC/DC converters and CHB inverters are mainly preferred by academia and industry.Such modular inverters still have a few problems that need to be fixed, including power imbalance between modules in the same phase.This could endanger the safe and stable operation of the complete modular inverter and cause over-modulation of some Hbridge inverter modules and grid-connected current distortion.This work proposes an AC voltage balancer to establish a power balance between nearby H-bridge inverter units in response to this issue.A better modulation approach is also described to achieve soft switching of VB.I.
Enhancing Movie Recommendation Systems with Deep Learning and Sentiment Analysis

In recent years, movie recommendation systems have grown in popularity as they provide a customized and effective approach for consumers to discover new films that cater to their preferences.Deep learning and sentiment analysis methods are used to improve these systems.Deep learning is a kind of machine learning that processes and analyses massive volumes of data using neural networks.Movie recommendation systems increase their accuracy and relevance by utilizing deep learning to analyse a variety of data sources, including user ratings, movie genres, and stars and actresses.On the other hand, sentiment analysis is the act of examining text to identify the feelings and view-points conveyed in it.Sentiment analysis is used in the context of movie recommendation systems to analyse user reviews and comments to ascertain the sentiment towards a specific movie.Systems for making movie recommendations better comprehend a person's tastes and emotional reaction by analysing the sentiment of user reviews.Neural networks are used as a method of merging deep learning and sentiment analysis into movie recommendation systems.Inputs like movie names, genres, and actors are used to train neural networks to predict user ratings and preferences.Sentiment analysis allows the neural network to take into account viewers' emotional reactions to certain movies, resulting in more precise and relevant suggestions.
Implementing precise geometric changes in a time-efficient manner to be employed in processing images in two and three dimensions

In this study, we provide a new technique for determining the geometry of objects and their potential uses.The suggested technique utilises a hybrid system that draws on the Standard Hough Transform, Bresenham's raster scan algorithm, and the Eigenvalues of the covariance matrix.This study takes use of the fact that certain properties of geometric primitives like straight lines, circles, and ellipses are correlated with the size and shape of the covariance matrix's eigenvalues.Regardless of the segment's length or orientation, a line in the continuous domain will always have zero for its tiny Eigenvalues.If the shape of the item is a perfect circle, then the object's big and small Eigenvalues will be equal.If the object is an ellipse, the semiminor and semimajor axes are represented by the small and large Eigenvalues, respectively.Therefore, in this work, the geometric primitives in images can be extracted by using the large Eigenvalues and small Eigenvalues of a covariance matrix.The suggested methodology is used in several computer vision and pattern analysis programmes.Automatic traffic systems often employ a straight line detection method to locate the lanes.Coins are identified using circular object recognition, while human faces are segmented and detected using elliptical feature extraction.
Mini-Review of Unmanned Vehicle Route Planning Based on Ant Colony Algorithm

Unmanned autonomous vehicles play an important role in the future transportation field.It has changed the control mode of traditional cars from the source, and improved the safety and efficiency of the transportation system by means of science and technology.The optimal path of driverless vehicles is the focus of path planning, and selecting a correct algorithm is also the key.Ant colony algorithm is selected to improve the algorithm on the original basis, and change the pheromone update mode and search strategy.According to the combination of actual road conditions, the shortest path is not necessarily the best path.This improvement can better deal with emergencies in road conditions.
Effectiveness of artificial intelligence algorithms in identification of patients with high-grade histopathology after conisation

The aim of this study was to compare effectiveness of various artificial intelligence classification algorithms in identifying patients with high-grade final histopathology of conisation based on last PAP smear result and risk factors for development of uterine cervical dysplasia and cancer. The data of 1475 patients who underwent conisation surgery at University Clinical Centre Maribor between 1993–2005 were analysed. Synthetic Minority Oversampling Technique (SMOTE) algorithm was employed for the imbalanced data correction. Various classification algorithms were tested with Weka open-source software. The 10-fold cross validation was used to define testing and hold-out set for analysis. Random Forest (RF) classification algorithm was better than the other tested algorithms and achieved 89.42% correct classifications (baseline ZeroR classification 63.4%, sensitivity 96.80%, specificity 76.60%, kappa 0.7632, Area under Receiver Operation Characteristic curve (AUC ROC) 0.911, Precision Recall curve (PRC) Area 0.916, and Matthews Correlation Coefficient (MCC) 0.771. Random Forest (RF) algorithm correctly identified majority of patients with final high-grade histopathology of conisation from patients dataset based on last PAP smear result and risk factors of developing high-grade dysplasia and carcinoma. Such algorithms can help clinicians to identify high-risk patients in future. An invitation could be sent to patients who did not participate in organized screening program, thus preventing the serious disease. Further studies are required in this regard.
A Novel Software Platform for Medical Image Processing and Analyzing

Image segmentation is a technique for extracting important features from a digital photo.It's a must-have for any serious image analyst.It may be used for medical diagnosis, analysis of satellite images, identification of faces and fingerprints, management of traffic, and many other tasks.It is a fresh, rapidly developing area of contemporary science.Since most diseases can be diagnosed and analysed with the help of medical imaging methods, image segmentation is particularly important in medical image analysis.Imaging techniques including X-rays, CT scans, ultrasounds, mammograms, and MRIs are routinely employed to acquire medical pictures.Image segmentation algorithms are crucial for transforming medical pictures into a form that can be understood by humans.Model training, data visualisation, human-computer interaction, and GUI development are only few of the many phases involved in developing an algorithm for medical picture analysis.Algorithm developers may save time and energy by using a software tool that handles the tedious details of the development process while they concentrate on writing the algorithm itself.Annotation of training data and graphical user interface (GUI) creation software is especially sought after for the creation of deep learning (DL) algorithms.
A Bayesian Multi-Stage Spatio-Temporally Dependent Model for Spatial Clustering and Variable Selection

In spatio-temporal epidemiological analysis, it is of critical importance to identify the significant covariates and estimate the associated time-varying effects on the health outcome. Due to the heterogeneity of spatio-temporal data, the subsets of important covariates may vary across space and the temporal trends of covariate effects could be locally different. However, many spatial models neglected the potential local variation patterns, leading to inappropriate inference. Thus, this paper proposes a flexible Bayesian hierarchical model to simultaneously identify spatial clusters of regression coefficients with common temporal trends, select significant covariates for each spatial group by introducing binary entry parameters and estimate spatio-temporally varying disease risks. A multi-stage strategy is employed to reduce the confounding bias caused by spatially structured random components. A simulation study demonstrates the outperformance of the proposed method, compared with several alternatives based on different assessment criteria. The methodology is motivated by two important case studies. The first concerns the low birth weight incidence data in 159 counties of Georgia, USA, for the years 2007-2018 and investigates the time-varying effects of potential contributing covariates in different cluster regions. The second concerns the circulatory disease risks across 323 local authorities in England over 10 years and explores the underlying spatial clusters and associated important risk factors.
A novel blind action quality assessment based on multi-headed GRU network and attention mechanism

Objective action quality assessment (AQA) is a complex machine vision task because existing AQA assessment models can’t effectively fit the subjective assessment. To address this issue, we propose a novel blind action quality assessment method. By processing the video data with spatial and temporal features, the performance of the model is effectively improved. In addition, we also proposed a new loss function to better train the model, which combines the information entropy of the data. Finally, the experimental results show that on the existing datasets AQA-7 and JIGSAWS are significantly improved, reaching 0.63 and 0.57, respectively.
Comparative study of gender recognition using speech

In this paper, we present a comparative study for gender and age recognition using speech signals.In this regard, we explored state-of-the-art deep neural networks to classify gender from speech features.We utilized two types of features, (i) time-series related features such as pitch, tone, intonation, etc., and (ii) transform speech features to spectrograms (using STFT and other techniques).We further evaluated the efficacy of each feature and deploy on deep learning algorithm (Resnet50).We also applied transfer learning for checking the accuracy of the dataset.The dataset utilized in our study is well-known speech dataset for gender recognition such as Common-Voice.We presented extensive results comparison with Sota techniques.
Empowering Salons Constituents through an Interactive Website for Graceful Glamour

This brief explains the concept of empowering salon members with an interactive website designed for the Graceful Glamor salon using HTML, CSS, JavaScript and MySQL.Graceful Glamour Salon aims to improve its services and interact with customers by creating an interactive website.The website will be designed using HTML, CSS and JavaScript to create a nice user interface and provide a good experience and usability.Through the site, lounge members will have access to a variety of features and functions.The Site will provide a variety of services for all customers, including appointment scheduling, online chat, and personal information.By integrating MySQL, a powerful and powerful database management system, the website will securely store customer information, programs and preferences.
Counterfeit Indian currency detection using convolution Neural network

The development of shaded printing technology has increased the rate of notes being copied for fake cash on a big scale.Despite the rise in popularity of electronic financial transactions and the recent decline in the use of paper money, banknotes continue to be widely used due to their dependability and simplicity of operation.A few years ago, printing could only be done in print shops, but today, anyone can print money paper with the highest degree of accuracy using a simple laser printer.As a result, counterfeit currency is increasingly a problem rather than real currency.India has decried issues like desecration and hidden money, and phoney currency is also a significant concern for the country.A deep learning-based framework is suggested as a solution to this issue in order to recognise the counterfeit Indian rupee.The false cash has been located using the MATLAB tool.As a result, it will be determined whether the Indian currency note is genuine or fake.Counterfeiting is the practise of making copies of legitimate currency.As a result, the Indian government does not support fake money.Only the RBI is in charge of printing money in India.The RBI must deal with the problem of counterfeit banknotes every year once they have been vetted and issued onto the market.The considerable technological developments in the printing and scanning fields contributed to the escalation of the counterfeiting issue.Therefore, counterfeit money affects the economy and devalues legitimate currency.The need to spot counterfeit money is consequently greatest.The vast majority of older systems relied on hardware and techniques for image processing.Finding phoney currency requires more work and is less efficient using these methods.We have suggested the Identification of Fake Indian Currency Using Xception Architecture to solve the aforementioned issue.Our method recognises fake currency by analysing the photos of the currency.
hand gesture recognization using media file architecture

In the real life system it s impossible to deaf and dump people for hearing and speaking an thing that's get related so in order to Get an what they want to be they will be in the position to handle this solution by this technique called media pipe that's the library what there in the open source that will give an accurate solution for the that in that we may use the pipeline for recognization activity in an python detection there will be the face ,hand ,eye detection is there in the programing for eye contrast there will be an high calculation of eye points and its complex when compared with the hand in the hand firstly its locate the palm and then it will show the result for the more complex task for an deaf and dump people like speaking inabilities will make possible to relate an hand gesture with sign that will show numbering and alphabet so that one can recognize the people easily by using this this makes the Opponent people to have the computer communication interface So that one can easily do the task easily By having idea of computer interface that will provide the communication medium by using this media pipe library For this it will capture the image and then one can figure out the pictures easily Here an American sign languages (ASL) that can be used to identify any thing easily through it by using an hand gestures.
Online Platform Construction of Legal Education and Intellectual Reform Based on Intelligent Legal Information Detection Algorithm and Intelligent Matching Model

Then, the background subtraction method based on Gaussian mixture model is used to extract moving objects, and the attributes of moving objects are determined through feature extraction.After matching with the normal model, a hybrid semantic similarity optimization fuzzy query algorithm is proposed.First, based on the similarity calculation concept, a new concept extraction method that is close to it is proposed; in order to meet the requirements of the future legal profession, schools not only need to offer elective courses to increase students' digital literacy, but also need to integrate digital literacy into legal knowledge, vocational skills and legal the whole process of ethics education.In order to meet the requirements of the future legal profession, schools not only need to offer elective courses to increase students' digital literacy, but also need to integrate digital literacy into the whole process of legal knowledge, vocational skills and legal ethics education.
Hybrid multi scale hard switch YOLOv4 network for cricket video summarization

Cricket is a popular sport with a lengthy duration that makes it challenging to watch in its entirety. Therefore, video summarization techniques are essential to providing viewers with a condensed version of the match's exciting moments. Automated cricket video summarization is difficult due to the sport's regulations and extended sessions. Existing methods often include repetitive shots, making the summary less concise and informative. Therefore, this paper proposes a hybrid video summarization framework that uses audio and text features to extract exciting clips from the raw cricket video. The framework employs the Multi-Scale Hard Switch YOLOv4 (MSHS-YOLOv4) network to accurately detect and label exciting events, including small details such as a ball hitting the stumps. A significance score is computed for each event to generate a summary that includes the most exciting and significant moments. The proposed method eliminates replay shots, reducing redundancy and making the summary more concise. The proposed method combines audio and video features to identify the most exciting moments, uses the MSHS-YOLOv4 network to detect and label exciting events, computes a significance score for each event, and eliminates replay shots to generate a concise summary. The proposed method outperforms existing summarization techniques in terms of accuracy, precision, recall, F1-score, and error. The analysis shows a significant increase in performance compared to the existing methods.