Reviewerly/exHarmony huggingface dataset:
Investigating the Role of Image Processing to Identify Pattern for Industrial Automation

In situations when both the output and the result are graphical, classification is used. The science's name was decided upon because of its concentration on image analysis. Imaging, satellite data, contrasts amplification, and voice signal production all could be done on the architecture here. A image or video cannot be recognized until a certain component or feature is found. In order to spot patterns, Cad system is used in this study. An area of study was the function of image processing in quality assurance. Even at high speeds., excellent ray tracing enables faultless record. Corporate machine learning is often used in process automation. Computer tech vision equipment includes things like camera-activating displays and movable video recorded tracks.
Research and Design of English Auxiliary Learning System Based on Human-Computer Interaction

Computer assisted English learning is a new field of English teaching assisted by computer and its software technology. With the development of computer technology and the popularity of the Internet, computer assisted English teaching has increasingly become an important means of English learning and one of the hot spots of information technology research. It will change the existing English learning environment and teaching mode, and greatly improve the efficiency of students' English learning. The research on English tutoring learning system based on intelligent algorithm is a research carried out by researchers of Ljubljana University in Slovenia. The purpose of this study is to find out how to use different methods to improve English teaching. The main goal of the project is to develop an intelligent algorithm to help teachers improve their skills and students' English teaching ability. Researchers also want to know whether there are some differences in English teaching ability among more or less trained learners.
Salient Region Detection in Images Based on U-Net and Deep Learning

Image analysis tasks use salient object detection because it not only identifies important elements of a visual scene but also lessens computational complexity by removing unimportant elements. In this research, we propose a novel salient object recognition method based on a deep learning network that maintains picture information in the mid and low regions. Using a deep learning model, our technique generates a coarse saliency map for the entire target image. The map is then fine-tuned utilising low-to-mid level information particular to the image. For detection of salient objects, we use a U-Net as our architecture. The saliency map can be predicted pixel by pixel, reducing low-level visual information loss. Our results show that our system regularly outperforms other approaches for detecting salient objects, resulting in superior precision and recall rates.
Face Recognition Using Support Vector Machine In MATLAB

The proposed work is to make use of the support vector machine algorithm in building a face recognition model using the MATLAB software. The efficiency of the face recognition model that is developed is tested by making use of two different image databases called the Faces94 database and the FEI database by considering a few images in both the databases. Apart from the images considered from the two databases, a few more images with some customized distortions were also taken into consideration to test the model. The main objective of this work is to check how the support vector machine algorithm with the aid of the bag of visual words module from MATLAB would work when used in the domain of face recognition.
An Image Processing Application: Design of VLSI based Bilateral Filter using Distance Matrix Method

The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VLSI-based image filter architectures are essential. Numerous applications based on VLSI architecture experience problems with significant size components that result in a filter design error during the floating-point arithmetic stages. The increased complexity of the filter design's component list is the issue encountered in the VLSI architecture. Noise removal often involves using the image filtering technique. There is a greater need for performance-based hardware sorting algorithms for low power embedded real-time image filtering applications. Noise often taints an image while being transmitted from one location to another. The purpose of the denoising technique is to reduce noise while keeping any potential signal features that may be significant. Compare to the prior method, the usage of multipliers can be reduced based on the techniques of distance matrix and hardware resource sharing. The speed of the proposed VLSI architecture for the bilateral filter can be faster than the existing approach.
Application of Data Mining Technology in Intelligent System of Machine Learning

In recent years, with the explosive growth of various types of data information, the demand for data information processing is also increasing day by day, so the data mining technology in machine learning has gradually been favored by more and more people, and it has been widely used in all walks of life. This paper mainly studies the application of data mining technology in machine learning intelligent system. In this paper, we propose a new learning framework for short and long time memory network (LSTM), it looks like a connection between different types of doctor by hiding a connection between neurons. The model also combines static factors such as age and gender with hidden state through full connection layer to realize personalized treatment. In experiments on real world datasets, the proposed algorithm achieves higher performance than other algorithms.
An Empirical Study of IoT Technology to Enhance Data Breaches and Critical Protective Methods via Various Correlation

The Web of Things (IoT), which aids in the creation opportunities to meet various business requirements, support in improving company activities, create and interact with the customers for effective delivery of goods and services, has seen overall expansion and development fueled by the dynamic business environment. The utilization of IoT and similar solutions has expanded, raising concerns about security vulnerabilities and the crucial actions that management must take to safeguard data and improve operational efficiency. The study focuses more on analyzing the key elements of IoT technologies that an organization may utilize to protect itself from security threats and take the necessary countermeasures to achieve sustainable growth. Each kind of network intrusion is thought to be linked around one or more different architectural levels; as a result, suitable authenticity, confidentially, and validation need to be established for greater protection. SPSS is utilized in the study's qualitative research design to analyses the data and offer insight based on the findings.
XSS: Cross-site Scripting Attack Detection by Machine Learning Classifiers

Cross-site scripting (XSS) is a menacing attack predominately marked by owsap10. It is primarily caused by insufficient sanitization of the web application's input and endpoint. Typically, developers are unconcerned about this issue, which prompts the attacker to carry out this attack. To detect this attack, this paper will employ multiple machine-learning classifiers like Logistic Regression, AdaBoost, Naive Bayes, XGBoost, Decision Tree, and will conduct experiments on the Kaggle Cross-site scripting dataset. After conducting numerous experiments, we discovered that the AdaBoost classifier performs 0.03 percent better than previous work and achieves 99.92% accuracy.
Big Data Approaches for an Accurate Evaluation of Cloud-Related Technology

A modern tech that has been utilized to ramp up complex calculations and marketplace procedures in several areas is increasing computer generated. Therefore, examination of massive data sets and cloud technology are necessary for “Greater computer” (HPC) to become a reality. Considering the advantages, the research tried to identify the benefits and disadvantages both of Having people and VMware. The research utilized SEM approach to assist the procedure for integrating advanced HPC. The regression models in this investigation were examined using Spss program. Any correlation between dependent and selected variables that has a significance level of 0.05, or “meaningful,” is regarded as significant. The response variable is Income Gain, whereas the study variables are HPC there in domain and year. Depending on the outcomes, there was no significant relationship between the independent and selected variables. The variables tested are not statistically meaningful, according to the Ips data, and the modelling data likewise does not meet the criterion.
Sports Image Classification and Application based on SIFT Algorithm

The research on moving image classification and application based on SIFT algorithm is an important research work. This is the first time to conduct this type of research in the field of computer vision. SIFT algorithm is a method for finding key points in an image. It works by finding edges that are similar to each other and then identifying the relationship between them. The more similar two images are, the closer they are on the image. The main purpose of this study is to develop a model that can classify moving images with high accuracy and low computational cost. This method uses SIFT feature descriptor to divide sports images into four categories: football, basketball, volleyball and football. This method has been tested with other standard methods.
Markerless Augmented Reality Application for Interior Designing

Markerless Augmented Reality - by using the screen of your smartphone, tablet, or AR glasses, you may place and examine digital things in the real world without the use of a marker like a QR Code, location, image, etc. Pokémon GO was one of the games that made AR a popular topic. Since then, several AR-enabled goods have been created, including social networking app filters, virtual footwear, sunglasses, and more. Often, we need to download applications such as Instagram or Facebook to view content. The introduction of web-based augmented reality implies that downloading a data-intensive and compute-intensive app is longer needed. Today, augmented reality material may be viewed through a browser. This web-based AR methodology is a new potential, due to its low bandwidth, accessibility, and speed. The advantages of WebAR are double. First, the restriction is too lax for viewers to access and share your content. People are more likely to browse and share content when an app is absent. This guarantees greater accessibility, increased connectedness, and a greater sharing feature. Because there is no application, there is minimal expense and flexibility, which leads to the second benefit. You can link to ongoing initiatives, for instance, by pushing fresh material quicker and more affordably. With our software, users may examine a specified 3D model, which is a representation of visual furniture without markers and can be viewed and altered in real-time. This is a step toward Web-based AR. This study suggests a novel method for applying augmented reality technology to furniture. Using a versatile online interface on a variety of devices, the user can connect 3D virtual furniture data and observe visual furniture.
Adaptive Multi Scale Products Threshold-Based MRI Denoising

Denoising an image has become an extremely important step in medical imaging, and it is performed throughout the entire diagnostic process. In medical imaging, it is imperative that a balance be maintained between the elimination of distracting noise and the maintenance of diagnostically relevant information. Imaging modalities have many objectives, one of the most important of which is to supply the doctor with the most reliable information possible so that they can make an precise diagnosis. The utilization of multiresolution noise filters in a wide range of medical imaging applications is garnering an increasing amount of attention. This study discusses some of the possible uses of new wavelet denoising algorithms for medical magnetic resonance images and reviews some of the techniques that have been used recently. These techniques were used to investigate various areas of the human body. The goal of this project is to demonstrate and evaluate various approaches of noise suppression that are based on both image processing and clinical experience. Rician noise is a phenomenon that is frequently observed in magnetic resonance imaging (MRI). In the field of medical image processing, edge-preserving denoising is becoming an increasingly important technique. In this paper, a wavelet-based multi scale products thresholding system is presented for the purpose of eliminating noise in magnetic resonance pictures. A dyadic wavelet transform that works similarly to an edge detector is used. As a consequence of this, significant features in images will continue to evolve with high magnitude throughout wavelet scales, whereas noise will quickly fade away. The wavelet sub bands that are next to one another are multiplied in order to improve edge structures while simultaneously reducing noise in order to take advantage of wavelet inter scale dependencies. When using the multi scale products, it is possible to differentiate edges from noise in an efficient manner. After that, an adaptive threshold is computed and applied to the products rather than the wavelet coefficients so that relevant features can be identified. Experiments have demonstrated that adaptive multi scale products thresholding is superior to conventional wavelet-thresholding denoising approaches in terms of its ability to reduce noise and retain edges. The fact that the wavelet transform can recreate an image without any noticeable loss of quality is the primary benefit of using this technique.
A Study of Cryptographic Primitives in the context of Blockchain's Data Integrity and Privacy

In Today's World, Blockchain is a promising Technology in all areas; things have also been drastically changed after COVID-19; challenges surfaced for implementing blockchain technology in the context of its computational complexity and security. After invention of it in 2008, cryptocurrency applications, i.e., Bitcoin it also getting introduced in Different applications. One of the Major Building blocks of Blockchain is Cryptography techniques. This paper discusses Cryptographic primitives used in different phases of Blockchain implementation and the challenges of implementing these cryptographic primitives. Bitcoin uses Cryptographic primitives to generate public-private key pairs and mining, while Ethereum uses Elliptic curve cryptography to create public-private keys and digital signatures. This paper divided uses of cryptographic primitives into three categories used in Blockchains, i.e., key generation Hashes, signature generation hashes, and proof hashes.
Spell Check Programme to Fix Mistakes in a Language

The study article provides a thorough explanation of how computer vision works. We can teach that we can remove faults from sentences via the use of computer vision. Grammar, grammar, syntax, usage, sequential words, dividing and consolidating, topic harmony, general word, unique verb, split verb, exceptional, undefined syllable, pronouns, etc tense are just a few examples of the many different sorts of errors that may occur. Every time we write a significant paper, we must ensure that it is error-free. Defects in the paper provide the visitor with a poor opinion. The phrase or phrase we write must be error-free above all else. We created a program that highlights problems in a paragraph or sentences when they are present and then prints the correct phrase or paragraph. Our programme is only functional in English. Our programme is called “Syntax Check.” Verifying the grammar of a phrase or a paragraph is helpful. Seven hundred words is the maximum allowed in a sentence or a paragraph. The language used to create our programme is Django. Our application will be made available without free. Therefore, it will aid writers in producing proper sentences and paragraphs. If you want to compose a perfect text, utilise a proof reader. Our programme exposes language flaws so that people may learn to compose perfect words. Our programme displays the mistake in red as well as the rectified mistake in blue. This will make it easier for users to see mistakes while producing significant content.
Measuring the Impact of Gamification on Kindergarten Students using Augmented Reality

Preschool is a primary teaching strategy that emphasizes sports, song, or other practical activities like painting and social contact. The term “kindergarten” is taken from a German word that may alternatively be translated as “kid's playground.” A tiny parcel of land is an essential component of the preschool, that is a location where kids may roam, cultivate, love, explore, evaluate, and discover while also connecting with natural. Each young person is unique in their behavior & hobbies. In a classroom setting, it is difficult for all pupils to give equal coverage to any and every topic that the instructor discusses with the class. While some students find the course conversations to be intriguing and stimulating, someone else might find them to be dull. To maintain a student focused on what could be talked or what is happening in class, there will be times when their attention has to be diverted. Consequently, it is among the biggest obstacles for a nursery school instructor. It is possible to successfully employ game-based learning (GBL) to enhance both teaching - learning process. Children are drawn to luminous screens quickly, and studies have shown that GBL fosters children's creativity. In this specific article, an AR (Augmented Reality) game-based classroom management is created to help preschool pupils acquire the fundamentals of learning, beginning with alphanumeric characters and gradually advancing into color. Preschool students participate in game-based instruction because young brains learn quickly when presented with visual material. Eighty children (aged 4–6) participated in the research, and they were split into two groups: the DGBL group (N = 40) as well as the Standard group of teachers (N = 40). Digital game environments were used to instruct the DGBL-based subgroup, while conventional teaching methods were used to instruct the pupils. Normal protocol. According to the findings of the trial, learning environments based on digital games have a definite beneficial impact on innovative thinking. The game makes the pupils visualize the ideas and keep them in their memories for a longer time.
Design of English Teaching System Based On KMean Clustering Algorithm

With learning through network has become an important way of autonomous learning. In the continuous improvement of College Students' English level, the traditional English teaching model cannot adapt to modern college English teaching. In view of this, the author constructs an English network teaching model based on basic web platform English learners and under the guidance of advanced theories and methods. This paper analyzes the tools and guiding ideology needed in the design of Higher Vocational on K-means clustering algorithm; On this basis, according to the characteristics and the actual needs of students, the design idea is obtained, the platform is constructed, and each module is designed and developed. The platform provides students with learning resources, online communication, online evaluation and information release functions; It provides teachers with the functions of uploading courseware resources, publishing homework, correcting students' last homework, and so on. Finally, the results and shortcomings of this platform are summarized. It also puts forward the objectives of the work to be done in the future and looks forward to the future research.
An Optimized Ensemble Model for Early Breast Cancer Prediction

Breast cancer is one of the pronounce cancer among females, following lung cancer despite constant efforts by developed countries. However, if the diagnosis is made in the early non-metastatic stage, it can be cured in 70- 80% of cases. Therefore, it is vitally important to detect cancer and predict the stage as accurately as possible. We proposed an optimal model to predict the chance of early breast cancer inheritance and to undergo further treatment as soon as possible. The features are trained using classification machine learning The performance of these traditional machine learning algorithms has the potential to improve. There is room for correction, so our aim is to optimize the prediction model to improve the performance. The results obtained with our optimized ensemble algorithm are quite satisfactory and improved with an accuracy of 83.07%.
Sentiment Analysis using Machine learning algorithm using speech signal

With the growth of online social networks, people now have a new forum for sharing their thoughts and perspectives with family, friends, and other users on various issues and topics. Users can express their thoughts and emotions in various forms like images, text, memes, postings, and audio/video messages, among which text is the most popular way to communicate on social media. In this study, we collected, tested, and analyzed the data from the most popular social media, Twitter. The primary goal of this work is to identify and assess the emotions and thoughts expressed by users in their text-based Twitter tweets. The Bag-Of-Words model, while the most popular technique for sentiment analysis, has two critical disadvantages, such as applying a manual lexicon for establishing word analysis. The second drawback is it analyses sentiments with high error because it refuses to acknowledge the language grammar impacts of the words and ignores semantics. In this research, we provide a unique approach for assessing online sentiments in a single domain and a solution for addressing crucial challenges in sentiment analysis that improves sentiment analysis accuracy. Using the improved bag-of-words model, word weight is taken to determine the polarity and score instead of term frequency. The proposed method automatically categorizes the keywords and characteristics related to scientific subject areas. This work provides an effective solution for typical sentiment analysis issues. The proposed model is enhanced to achieve maximum sentiment analysis precision.
A Comprehensive Review on Real Time Object Detection using Deep Learing Model

Object recognition is a technique to identify and detect distinguish objects in complexed background images and videos. The technique is typically based on machine learning or deep learning to obtain the accurate performances. When an image or video is observed, the object they are interested in is immediately recognized and identified. The goal of object recognition is to use a computer to replicate this intelligence. The natural progression of image classification is the classification and localization of the subject of the image. This work analyses the most recent techniques used for the object detections and gives a complete insight about the possible upcoming advancements in deep learning model with improved detection accuracy.