Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Exploring Opinion Mining Techniques in Web Data

Web utilization digging is fundamental for the Cross-Area Opinion Arrangement (CDSC) on the grounds that it might ensure client fulfillment concerning their collaborations with the organization. By accessing a specific Site's log record, web use digging is likewise helpful for recognizing or improving the guests to that webpage. The investigation of web utilization from log information from an instructive organization is the principal subject of this exploration. The utilization of information mining is called Web Use Mining (WUM), once in a while known as web log mining. To improve electronic applications, WUM methods are applied to huge volumes of information to extricate sagacious and interesting examples from Web information, especially from web logs. Information source, pre-handling, design recognizable proof, and example investigation are the four phases of web utilization mining. Subsequent to finishing these four stages, the client can find the important utilization examples and utilize this information for their specific purposes in various ways, like further developing the web application, perceiving guest conduct, drawing in new clients, continuing to exist ones, and so on.
Comparison on Video Streaming Intervention on Neural Network QOE Problem Statement Technique

Adaptive HTTP streaming has emerged as a new trend to aid in the adaptive delivery of video. An HTTP streaming consumer wants an exact estimate of the number of available resources and the name of the resources they'll be using. A lot of people are interested in HTTP adaptive streaming (HAS) these days, and it's now employed by most video streaming services, including Netflix and YouTube. HAS, the use of dependable delivery protocols, together with TCP , does not be troubled by way of photograph artifacts because of packet losses, that are common place in the traditional streaming era. For that reason, the QoE fashions advanced for different streaming eras by myself aren't sufficient on this paper, we attended at the maximum critical resource it really is bandwidth with the manner of neural network. A modern and current component for throughput estimation is provided by considering prior values of instantaneous throughput and round experience time. However, for the first time in HTTP streaming, we introduce the usage of bit rate estimate. Experiments show that our method can handle significant changes in bandwidth and video bit rate.
Secure Translation from Sketch to Image Using an Unsupervised Generative Adversarial Network with Het for AI Based Images

Sketch to image translation could be a useful tool in identifying person and generating real life-like pictures out of sketches and drawings. Translation of sketches to image can come in handy in comic generation or graphic designing applications. The existing system under perform with the accuracy of the images generated and generating images with desired background image is also an important area to improve on. So, it is essential for a system to produce images with better accuracy and quality with desired background. The sketch to image translation is generally deployed using Convolution Neural networks (CNN) and Generative Adversarial Network (GAN) but the solutions often fall behind and fail to make high quality or a real life like output. While these methods focus on bringing out images resembling the given sketches, they fail to concentrate on quality of images generated. The images generated from existing Sketch to image translators and not real life-like and the generators themselves underperform in terms of accuracy. So, it is essential for a system, which can translate sketches into images without labels with better accuracy. The quality and the integrity of the sensitive images should be protected by using Homomorphic Encryption Technique (HET).
Image Quality Enhancement and Caption Generation

Our brain is capable of labeling or annotating any image that is visible to us. But the machine does not have the capability of thinking on its own. To bringing it possible, we are employing Deep Learning Techniques. The aims of image captioning are to produce natural language expressions, to capture the relationship between the objects in the image, and to evaluate the credibility of the constructed descriptions. Uprooted features of the image are handled by Xception. Xception is a CNN model. These extracted features will be permitted by the LSTM model, and it will allow for the image subtitle. When the descriptions provided are just one word long, the object detection task for image captioning becomes more generic. But the problem arises when the image is of poor quality. The Images taken by a user under various lighting circumstances, Dots Per Inch (DPI), and different angles may have worse quality. We are utilizing several techniques, object identification, image classification, CNN, and OCR, to enhance the quality of images.Reducing Noise, Rescaling, Binarization / Thresholding, Removing Skewness / Deskew, and Morphological Operations are a few actionstaken during the image quality enhancement process.
Sign Language Recognition Using OpenCV and Convolutional Neural Networks

Deaf and dumb people all over the world utilize sign language as a means of communication. Gesture detection is regarded as a challenging task in computer vision since it is complex for varied shapes. This research describes the application of Convolutional Neural Networks (CNN), an artificial intelligence technique, to recognize sign language gestures. The technique is a continuous selfie mode sign language video. The use of sign language with neural networks is superior to the gadget method because it is quicker and more precise. It provides a method for establishing two-way communication without the requirement of a translation. The HSV color technique is utilized to both render the background black and to identify hand gestures. The techniques used by the images for accuracy include grayscale, dilation, and mask operation. Regardless of skin tone, CNN utilizes filters of varying sizes to recognize the symbols. CNN is used to separate the photos. Using the CNN method, 10 American gesture alphabets and 20 Italian languages are recognized.
Tracing the Motion in High-Speed Video Frames Using Deep Learning

One of the most important jobs of a self-driving vehicle's visual system is to identify and categorize obstacles along the route. Cars need to be detected, categorized, localized, and linked across frames if they are to be tracked. With the low cost and high utility of visual information, vision-based technologies have gained popularity for this purpose. In this paper, we develop a deep learning-based motion tracking from high-speed video frames. The model is developed using deep convolutional neural network that makes it to be applicable for classification of objects in the video frames. The deep convolutional neural network classifies the obstacles present in the pathway and offers a better classification pattern than the conventional models.
Analysing the Customer Behaviour using Reinforcement Learning for Business Development

This study focuses on leveraging reinforcement learning techniques for analyzing customer behavior to drive business development strategies. In the modern competitive landscape, understanding and predicting customer preferences and actions are crucial for sustaining growth. Reinforcement learning, a branch of machine learning, offers a unique approach to model customer interactions and optimize decision-making processes. This paper explores the integration of reinforcement learning algorithms into business environments to create adaptive strategies that respond to customer behavior in real-time. Through a synthesis of existing literature and empirical research, the study aims to showcase the effectiveness of reinforcement learning as a tool for understanding and leveraging customer behavior dynamics in a rapidly evolving business landscape.
Performance Analysis of Solar Photovoltaic Panel using Machine Learning

The electrical performance of solar cells depends on factors like intensity of solar radiation, panel tilt angle, elevation, wind speed, ambient temperature and few other parameters. The panel temperature increases during higher solar insolation leading to a drop in electrical power output. This work includes the research of output maximizing angles of PV panels - solar azimuth angle and zenith angle. The inclination is made according to the location and latitude of the study area so as to obtain optimum operating conditions. Optimization methods for achieving efficient electrical output have been developed by studying various supervised machine learning and deep learning algorithms. The most important parameters from the gathered real-time data are obtained. DenseNet neural network architecture consists of dense connections of input layers used to determine the impact factor of various measured parameters. Outdoor experimental investigations confirm that the optimal orientation of solar panel will maximize the solar radiation captured by it and hence performance is enhanced.
A Lossless Steganography Framework: Implementation and Evaluation

Modern infrastructure requires advanced security measures to protect computer interactions. Network security is becoming increasingly important due to the majority volume of documents exchanged over the internet. Privacy and integrity of data are crucial to secure document from unwanted entry. This has led to a rapid growth in the area of information hiding, also called as steganography. Data hiding techniques are receiving significant attention due to concerns about piracy and patent infringement. Patent owners want to ensure that their intellectual property is not illegally accessed or used. Digital signatures can be used to secure electronic materials, such as songs, movies, books, and software. However, simply encrypting data is not enough to provide next-level security. To enhance security, data can be hidden within other files, such as images or videos, using steganography. This makes it more difficult for unauthorized individuals to detect and access the hidden data. Steganography can also be used for covert communication, allowing individuals to exchange messages without detection. The use of steganography can help protect sensitive information from cyberattacks and data breaches. This is important for businesses, governments, and individuals to examine the threat of cyber risks and take relevant steps to secure their information. Advances in technology are constantly changing the security landscape, requiring organizations to stay vigilant and adapt to new risks. By incorporating steganography and other advanced security count, institutions can make certain the integrity, confidentiality and accessibility of the information. The proposed work consists of to inculcate the Secret image (B) into Protection image (A) via substituting LSB bits of Protection Image. The resultant copy is known as Stego-object, applying DCT to convert Stego-object from three-dimensional dominion to frequency and finally compressing the Stego-object by means of Huffman compression.
Gesture Recognition using Cognitive AI and Deep Learning

Real-time hand tracking differs greatly from RGB video tracking of frequently monitored objects. Because it is a biological object, the hand experiences both physical and behavioral changes as it moves. If a basic RGB camera is used to record the video, the hand also occupies a very small portion of the image frame. It has an unpredictable pattern of movement, which greatly degrades the image quality. We provide a hybrid architecture in this chapter to monitor hand motion in RGB sequences of video. The framework incorporates the distinctive characteristics of the Scale-Invariant Features Transform (SIFT) method and Residual Network-based Rapid Region-based cognitive network (Faster R-CNN). Deep neural networks' discriminative learning capacity and SIFT's hand-crafted features' quick detection speed enhance this combo. As a result, our online technique adjusts to variations in hand movement that occur in real time and has a high level of cognitive hand trajectory detection efficiency. The empirical findings presented in this research show that the method is capable of handling both the inherent and extrinsic difficulties involved in the visual observation of hand motions in RGB films.
Gesture Based Hand Control System for Power Point Presentation

The rise of ubiquitous computing has increased the role of computers in our daily lives. Due to its versatility and user-friendliness, hand gesture recognition is one of the active research areas in the field of human-computer interaction. With the aid from hand movements or other physical motions, users can interact with computers or other electronic devices without using a keyboard, mouse, or touchpad as a physical input device. A system that can be used to deliver information to persons who have disabilities or to operate equipment was developed using gesture recognition technology. In our system, the application of pointing behaviour for the human-machine interaction is our primary concern. Today, the majority of human-related electronics are still in the developmental and experimental stages of implementing gesture recognition. Presentations are very important in a number of aspects of life. For the purpose of developing and managing presentations in the power point, we have explored a dynamic recognition of hand gestures ‘system in this work. By utilising some simple python library functions which use mainly machine learning algorithms, the system is made possible with the gestures of hands for the slideshow’s control. Users of the suggested system can control the SLIDESHOW Presentation using simple gestures without ever touching the PC.
Analysis of Various Learning Approaches in Occluded Face Recognition

Face identification is a complex problem for robots, even though it is a simple human activity. There is a high growth of computer power aids in the rapid improvement of facial detection technologies. Several intelligent algorithms have been suggested for facial detection. So far, there's very less focus on thorough examining towards the existing techniques. This work aims to present a comprehensive analysis on face detection systems. First, an extensive review is made over the wide range of existing face recognition techniques, including their origin, working mechanism, advantages, limits, and applications in domains other than face identification. Next, the existing works for feature analysis and other techniques are made. Finally, this study presents an extensive evaluation amongst some of the algorithms exemplified to have an over all-encompassing perspective. Some previously published survey articles on face identification algorithms focused solely on technical specifics and commonly used techniques. However, our study thoroughly discusses face identification strategies and contemporary neural network techniques. This study provides the benefits and disadvantages of the state of art methods and provides a literature study that encompasses their application beyond face detection.
Optimizing Kidney Ultrasound images through Pre-Processing Filters

Medical image processing and analysis have greatly advanced in the past decade, significantly contributing to the diagnosis of various diseases.However, It is crucial to address the need for effective data management in the medical field due to the significant rise in data generation and storage. It necessitates the exploration of compression methods as a means of achieving efficient data handling. Consideration should be given to image processing approaches to minimize redundancy. Ultrasound imaging has gained importance in recent years, but the presence of artifacts in ultrasound images has complicated diagnoses. An evaluation has been performed to identify appropriate Pre-processing techniques for kidney images before extracting kidney features. Observing the sensitivity and calculating the PSNR and MSE of the filtered image are used to assess the applied methods. The results indicate that the median filter is ideal for image quality enhancement, while the Sobel filter is highly effective in detecting kidney edges.
An Experimental Analysis of the Software Bug Prediction and Identifications Approaches with Different Levels of Inheritance

The ever-increasing complexity of software systems is being exacerbated by the rise in the number of flaws. The waste of resources caused by bugs has had a negative impact on the software development process. To speed up the software development process and avoid rework, it's critical to discover and foresee issues. The researchers were able to identify metrics (product, process, and project) as the primary sources of the issues. The researchers hope that by doing so, they can raise developer understanding of the most common sources of defects while also supporting a smooth software development process. Furthermore, the effectiveness of procedures connected to bug severity prediction according to degrees and levels of inheritance has been correlated by the researchers. With a strong link, product metrics-oriented software problems have a considerable negative impact on software quality. Different levels of inheritance are linked to different bug severity levels, with the severity levels rising as the levels of inheritance climb. Software developers, software quality specialists, and researchers will benefit greatly from the findings of this study. The findings of this study, while recommending a set of methods to justify inheritance, suggest keeping an eye on the severity of flaws in order to deliver software of acceptable quality. The findings of this study also point to a rationalisation of efforts to identify and anticipate problems.
Ensemble Learning Model for Gender Recognition Using the Human Voice

Gender recognition through the human voice is a rapidly growing field of research. It is a vital research subject as the human voice now has an ever-growing list of applications, especially for social good. Gender recognition using voice is beneficial in multiple applications such as online healthcare systems, interactive services, crime analysis, security systems, etc. Algorithms have already been deployed, but accuracy and efficiency can still be improved. This work aims to classify human gender based on human voice data between males and females. It uses modified ensemble techniques based on classifiers such as k-NN (k-nearest neighbors), Random Forest (RF), and SVM (support vector machine). In this study, a benchmark dataset is used which includes 3168 instances and 21 attributes, where 20 attributes are the predictors, and one attribute is the target – ‘male’ or ‘female’ as instances. To evaluate the proposed model’s results, precision, accuracy, recall, and F1-score were calculated. When compared to traditional machine learning models employed independently, the ensemble model imposed better results, with an accuracy of 99.05%.
Mimicking 3-DOF Human Upper Neck Movement using Wearable MPU6050 IMU Sensor-Based Device with Tilt Compensation Algorithm

This research proposes the development of a robotic neck with three degrees of freedom (3-DOF) that mimics human upper neck movements. The control system utilizes an MPU6050 IMU-based controller attached to the robot's body, head, and lower neck. Building upon previous work, the study addresses limitations in tracking head movements independently of the user's body and lower neck orientation, encompassing pitch, yaw, and roll. The researchers employ the MPU6050 IMU sensor and a tilt compensation algorithm to determine the angles of both the robotic and human upper neck. Data collected by Arduino and LABView Software from the system is analyzed using statistical methods with JMP Software. The results demonstrate that the robotic neck effectively acquires and imitates human upper neck movement, with a significant reduction in angle difference errors compared to the previous system.
Car Drive Simulation in Metaverse with VR Glasses for Testing Driver’s License

Obtaining a driver's license involves significant expenses, location constraints, and potential accident risks. Virtual Reality (VR) technology and driving simulations have been employed to mitigate these challenges and enhance driver preparedness. The research approach integrates VR glasses, a Logitech G29 controller, and a computer running Blender and Unity software to create a realistic driving simulation. The Blender program is utilized to construct diverse models and scenes, while the Unity program facilitates the creation of an immersive driving experience using the available assets. The proposed system simulates three essential assessments required for practical driving license tests: forward and backward driving, reverse parking, and pedestrian interaction. The collected data from an experimental trial involving 11 participants performing these assessment scenarios. Notably, the subjects expressed high satisfaction with the driving simulation, with 70.5% stating that the virtual experience can effectively develop the necessary skills required for the driver's license test.
Optimizing Real-Time Path Planning for NPC Navigation: Leveraging CentA* Algorithm to Enhance Efficiency and Adaptability

Modern game development heavily relies on AI pathfinding to enable non-player characters (NPCs) to navigate the game world efficiently. The use of pathfinding algorithms is essential for creating a believable and immersive gaming experience. A popular choice among the various pathfinding algorithms used in game development is the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm, which combines heuristic and graph search techniques to find the optimal path for NPCs. In this paper, the CentA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm is introduced as an extension of A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> that enhances the selection of starting and ending nodes, triggering NPC behaviours before reaching their destination. The gaming model is built using Unreal Engine 5 Blueprints, which incorporates the pathfinding algorithm and detection of enemy behavior that can be reused for different creature types with various character models. The pathfinding algorithm ensures that NPCs move in a realistic and efficient manner, enhancing the overall gaming experience. The introduction of CentA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> offers an improvement over the traditional A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm. A comparison of the proposed algorithm is done over the other existing algorithms and a minimization of the overall tour length is encountered. Compared to other existing algorithms, the proposed algorithm offers superior performance by reducing the overall tour length by a minimum of 0.67%. Although the proposed algorithm may lead to a slight increase in tour length by approximately 1.42%, it remains time-efficient and computes fewer blocks than other algorithms. These results demonstrate the potential of the proposed algorithm for optimizing tours in various contexts such as route planning and logistics. This paper emphasizes the significance of pathfinding algorithms in game development, particularly in creating a realistic and immersive gaming experience for players. The implementation of CentA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> has significantly enhanced the behavior of NPCs in games.