Reviewerly/exHarmony huggingface dataset:
Signal Enhancement Methods Based on Wavelet Transform, Fractional Fourier Transform and Short-time Fourier Transform

This review paper mainly focuses on different signal enhancement methods such as wavelet transform, fractional Fourier transform (FrFT) and short-time Fourier transform (STFT). First, this paper introduces the concept and importance of signal enhancement, as well as some current issues and challenges. Then, in recent years, the application of the three methods in wavelet transform, fractional Fourier transform and short-time Fourier transform is described. In terms of wavelet transform, this paper discusses the application of wavelet function to image enhancement and the specific steps, and analyzes its advantages and application in signal enhancement. In terms of fractional Fourier transform, this paper introduces its difference from traditional Fourier transform, and discusses its application in combination with adaptive filtering technology and the application of multi-level FrFT in the field of speech enhancement. Finally, in terms of short-time Fourier transform, this paper discusses its application in the fields of image enhancement and speech enhancement. The review paper finally discusses the characteristics, advantages and limitations of these three methods in signal enhancement, and looks forward to the future research directions. Through the research of this review paper, it can provide some reference and guidance for the research in the field of signal enhancement.
Advancements in Music Generation: Leveraging RNN and LSTM Networks for Automated Composition

Historically, music has been considered an analogue signal and was manually generated by humans. However, in contemporary times, technology has made it possible to automatically create instrumental compositions without mortal intervention. To achieve this, specific challenges need to be addressed, which are thoroughly discussed in this paper. The paper begins with a concise introduction to music and its various factors, citing and analyzing related work conducted by different authors. The primary objective of this paper is to propose an algorithm based on recurrent neural networks (RNN), specifically long short-term memory (LSTM) networks, for the generation of musical notes. By implementing this algorithm, we aim to explore the potential of machine-generated music and its implications in the modern era.
Implementation Of Natural Language Processing for Spam Email Detection in Outcome Based Education (OBE) Application

The Natural Language Processing (NLP) approach has been proven to be effective in spam detection in e-mail because of its ability to process text and identify patterns and distinctive characteristics of spam e-mail. Methods in this NLP approach include data pre-processing, such as removing punctuation, irrelevant common words, tokenization, stemming, and others, as well as classification techniques such as Support Vector Classifier (SVC), Naive Bayes, and others. In testing various models, there is one model that shows the highest precision with the number 0.98. This study shows that the NLP approach provides better performance in spam detection compared to other methods. However, it is necessary to improve technology and develop more complex detection methods to improve the performance and accuracy of the email spam detection model
A Cognitive and Metacognitive Diagnostic Assessment System: TATSUJIN Test ICT

In this paper, we present the TATSUJIN Test ICT, an assessment tool developed to visualize students' academic difficulties. The original TATSUJIN Test was designed by Imai et al. [1] as a Cognitive Diagnostic Assessment test at a wide range of elementary-school-age children. This test reveals inference abilities in elementary students, supported by schemas and executive functions. Currently, efforts to improve teaching based on the academic difficulties unveiled by the TATSUJIN Test are yielding substantial impact. Receiving voices from junior high school teachers that they wish to use the TATSUJIN test for their students, we adjusted the test questions for junior high school students and developed the TATSUJIN Test ICT. This version allows students to answer the TATSUJIN Test in a computer-based testing (CBT) environment. We conducted a survey in public junior high schools with students aged 13-14 (N=335). The results showed that junior high school students face struggles similar to those of elementary school students. Additionally, we found that numerical intuition could be a suitable proxy for general cognitive ability, and we could identify problems more susceptible to the influence of prior knowledge. Our findings provide a new perspective for a deeper understanding of the cognitive abilities and difficulties of junior high school students.
A Real-time Distance Learning System for Alpine Skiing Using Virtual Reality

A surge in interest in distance learning has been prompted by the COVID-19 pandemic. However, the acceptance of distance learning systems in skill acquisition has been limited because of the potential gap between students’ ability to acquire skills remotely and their application of those skills in an actual environment. Although various e-learning systems for skill acquisition have been introduced in education, most have focused on interactions between the users and the technologies rather than real-time interaction. Furthermore, taking skiing as an example, the constraints imposed by the environment have made it particularly challenging to achieve real-time interaction in ski training. Although several studies introduced various skiing training systems, most of them did not focus on the interactions between users and instructors. To address these gaps, this study proposes a prototype training system for alpine skiing and presents the results of two experiments conducted on distanced ski simulators. In the first experiment, we compared connection delays between TCP and UDP connections to establish a real-time interaction. The second study compared the effectiveness of the training between the conventional training system with a video and the proposed remote training system with a 3D model displayed in the virtual environment using the Wizard of Oz methodology. The results suggest that the proposed distance learning system significantly improves users’ line-tracing skills in parallel alpine skiing.
Decentralized Training for Secure Network Security: Leveraging RFB Protocol and Federated Learning in Zero Trust Network

This study proposes the implementation of a Remote Frame Buffer (RFB) gateway in a zero-trust network using Federated Learning. The proposed approach allows multiple remote devices to collaboratively train a machine learning model without the need for centralized data storage, and without compromising the privacy of the data owners. The study includes an evaluation of the effectiveness of RFB with and without Federated Learning, as well as the effectiveness of local and global datasets and Gradient Stochastic Descent. The results demonstrate that the use of Federated Learning with RFB protocol is more effective than using RFB without Federated Learning for training machine learning models. The decentralized training process ensures that the data of individual devices remains protected, without the need for centralized data storage. Homomorphic Encryption and Differential Privacy techniques can be incorporated into the decentralized training process to enhance security.
A Novel Approach to Detect Falls Using an Iot-Based Health Analysis System

Fall detection is a major issue in public healthcare, particularly for the elderly. When their physical condition deteriorates, prompt and dependable care is necessary to mitigate the negative impacts of cascade. The current system, designed by others, is incapable of detecting false falls and lacks a health analysis system. The proposed system monitors a person's activity, employs an effective quaternion algorithm to detect a deviation from typical diurnal conditioning, and then automatically transmits a request for assistance along with the case's location to the carers. These models were initially developed to distinguish fall from daily movements such as walking, moving, sitting, standing, reclining, and bending.
PolyDiseaseNet: A Multimodal Machine Learning Approach for Comprehensive Disease Diagnosis and Prognosis

Cancer is a leading cause of death world wide, accounting for an estimated 10 million deaths in 2020 alone. Early detection and accurate diagnosis are essential for improving patient outcomes and reducing cancer- related mortality. However, traditional diagnostic methods, such as biopsies and imaging, can be invasive, time- consuming, and expensive. Deep learning, a subfield of artificial intelligence, has emerged as a promising tool for cancer prediction and diagnosis. Deep learning algorithms can be trained on large datasets of gene expression data to identify complex patterns that are associated with diverse types of cancer. Once trained, these algorithms can be used to predict the type of cancer that a patient has based on their gene expression profile. Keras, a high- level Python library for TensorFlow, provides a user-friendly framework for building and training deep learning models. Keras offers a variety of pre-built deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are particularly wellsuited for image analysis tasks, while RNNs are well- suited for sequence data analysis tasks. Gene expression data is a rich source of information for identifying cancer-associated patterns. Gene expression data reflects the activity of genes within cells, and different genes are expressed at distinct levels in diverse types of cells and tissues. By analyzing gene expression patterns, deep learning algorithms can identify subtle differences between cancer cells and normal cells. This study investigated the application of deep learning using Keras for cancer prediction and diagnosis, with a primary focus on five prevalent cancer types: colon, breast, lung, kidney, and prostate. The study utilized gene expression data from a large cohort of patients to develop and evaluate deep learning models. Various deep learning architectures were explored, including CNNs and RNNs. The performance of each architecture was evaluated on a held-out test set. The bestperforming model achieved a classification accuracy of over 95% for each of the five cancer types. The findings of this study demonstrate the potential of deep learning using Keras to improve cancer prediction and diagnosis. The developed models achieved high classification accuracies, surpassing traditional methods. By analyzing gene expression data, deep learning models can identify specific genetic mutations or signaling pathways that are driving cancer growth and progression. This information can be used to select targeted therapies that are most likely to beeffective for each individual patient.
Design and Implementation of IOT Based Real Time Traffic Monitoring System

In modern world, traveling has become a very stressful task due to the unchecked population boom. People grow irritated and frustrated as a result of this, which also expensive. Numerous solutions have been proposed by researchers to address this issue. The timer model is one of the models that is currently in use. Timers may be utilized extensively to regulate traffic at each step. Another solution in use makes use to identify cars generate the necessary signals. This research presents a revolutionary image-processing -based that maintain under. Matlab 's tools, a count of vehicles from the input images is calculated, and a green signal allowing vehicles to pass is given at various times based on the count. LEDs are used to show the red and green signals in the proposed prototype, and a seven-segment display is used to show the green signal's decrementing timer.
Mobile Robot Self-localization and Table Tennis Detection Via RGB-D Camera and YOLOv8

In order to improve the training efficiency of table tennis players, many robots have been applied to various physical training scenarios. Among all these studies, the ball picking robot can save players time for picking up the ball and avoid fatigue caused by bending. However, a table tennis picking robot needs to know where it is, where the ball is, and where the table is. In this way, the mobile robot can move towards the ball, pick it up, and return to the table. In this paper, we propose an approach to solve all these problems by using an RGB-D camera and YOLOv8 object detection model. Firstly, the mobile robot is steered to build an occupancy grid map of the table area. Then, based on the known robot pose, the mobile robot moves towards an assigned position, captures an image from the direction. Sift features are extracted and saved as the reference map. Finally, during the usage phase, the mobile robot localizes itself according to the reference map, detects and recognizes a table tennis on the ground, moves to grab the ball, and returns to the table. The conducted experiment shows that our approach is effective and achieves better localization performance. Based on the localization information, the mobile robot moves through multitarget points along the planed route to detect the table tennis.
Sampling-Based Path Planning Driven by Transformers with Direct Sampling in Ellipse and Region

Path planning problem aims at finding a collision-free path between a starting vertex and a goal vertex. Recently, neural-network-driven path planning algorithms have been developed to predict a region containing an optimal path, which subsequently serves as a direct sampling domain for sampling-based path planners. However, low-accuracy predictions hinder the further development of neural-network-driven algorithms, leading to long computation time and inefficient sampling with a uniform sampler. In this paper, we first design a transformer-based model, namely improved segmentation transformer (ISETR), involving a segmentation task and a regression task. A region containing an optimal path and an estimation of the path length are processed separately. The outputs of the ISETR are employed to realize a direct sampling for subsequent path planning, which is composed of regional and elliptical domains. Distinct from other neural-network-driven algorithms, the elliptical domains derived from the estimated length replace the uniform sampler, guaranteeing a probabilistic completeness of the proposed sampling-based path planning algorithm, ISETR-RRT*. Ablation studies verify the superiority of the ISETR in the accuracy of predictions. Additionally, the ISETR-RRT* outperforms other transformer-based path planning methods and an informed RRT*, in terms of computation time, path length, and sampling numbers.
Practical Quantum Game Theory

Rapid developments of quantum computers allow us to test the theoretical concepts but are limited by the small number of qubits. We study game theory from the quantum algorithm perspective not only because of their unique properties, but also because of their relatively simple form, which is not constrained by this limited hardware. We discuss the practical results of running quantum games on IBM Q cloud computers and the limitations of their use in business and economics.
Progression Study on Privacy-Preserving Big Data Publishing Techniques

In today's virtual world, it is vital to preserving privacy wherein the information is amassed from different sources with sensitive information at an exceptional level had been the benefits may be gained from the big data by publishing the data for research without jeopardizing the privacy of an individual personality. Traditional anonymization techniques have been used to protect personal information of individuals for research or market decision-making purposes. However, these techniques are not suitable for Big Data due to its large volume, high velocity, and variety of data. Advanced techniques are necessary when working with Big Data because traditional techniques cannot handle the storage and analysis of large amounts of data. The aim of this paper is to ensure privacy in Big Data by conducting a comparative study of various techniques.
Sky Sweeper: A Drone Survelliance Model Using YOLOV8 and Jetson Nano for Plastic Waste Monitoring System

The recent statistics reveals that our country India produces 22 million tons of plastic every year. It has been noted that the inappropriate collection and segregation of plastic garbage makes its disposal a severe concern. The major issue with plastic trash in India is not about how much is produced, but rather inefficient waste management practices, such as ineffectual collection and recycling. The urgent need to address the mounting environmental issues caused by litter and garbage in outdoor spaces, particularly in places like beaches, is what spurred the development of a trash detection drone surveillance model. The drone surveillance model's particular objectives include Trash Detection. Real-time monitoring, Geo-location and mapping, Notifications and alert system, data integration and analysis, efficiency and Cost effectiveness. The proposed UAV surveillance model rightly locates the most plastic populated area through YOLOV8 (You Only Look Once) object detection algorithm and then the deep learning algorithm is applied on the captured data to explore the right location and the location report will be forwarded to the concern official through the digital notification by integrating the model with NVidia Jetson Nano Kit to assist in delegating the workforce to the appropriate location thereby utilizing the available manpower in an efficient way. Also results of the proposed model is compared with YOLOV5 implementation and existing Ensemble models where it is concluded that the proposed YOLOV8 model provides higher accuracy comparatively.
Dynamic Price Management System in a Distributed Cloud Environment

With the increase in the number of e-commerce applications on the internet, the demand and supply of commodities have increased. Though a static pricing system can be used in today's evolving market, prices of a commodity ought to be dynamically updated to improve revenues. The main challenge is to design a system dealing with real-time price updating based on the analysis of the available and complex data. The fundamental challenge is to design a dynamic price management system that meets the demand of customers and the availability of a commodity to maximize revenues with minimum cost and time involvement. In this paper, a Dynamic price management system (DPMS) for a flexible and connected smart nation has been designed to control and monitor the pricing of a commodity from selling platforms like retail, wholesale, the company's website, and e-commerce. Enormous pricing management system models have been researched, but in this paper, our objective is to provide a flexible, versatile, and intellectual pricing management system that handles the pricing of a commodity through a single interface for all the selling platforms. Smart pricing is the dynamic adjustment of prices or repricing of a commodity based on the market demand for a product. This model simplifies the vendors' problem of controlling a homogenous commodity's price component on different selling platforms like retail, wholesale, and e-commerce for a fully connected smart nation.
Animal Collision Detection in Real Time to Prevent Accident using KNN Classifier in comparison with Stochastic Gradient Descent

This project aims to assess and improve the detection of animal collisions in real-world situations. Materials and Methods: To forecast animal collision detection in real time and avert accidents, KNN and stochastic gradient descent are used with different training and testing splits. Both algorithms which are used in this study underwent 10 iterations, resulting in a total 20 iterations. The sample size for the study was 1347 and was sourced from the kaggle website, with the G Power as 80% (g power setting parameters: <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha=0.05$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{power}=0.80)$</tex> . Results: K-Nearest Neighbor (79.82 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) has the increased accuracy over Stochastic gradient descent (75.19%) with a significance value of 0.039 (p<0.05) for both accuracy and loss, which is statistically significant and there is no insignificant differences between the two algorithms. Conclusion: The accuracy percentage for K-Nearest Neighbor algorithm is 79.82% whereas the accuracy percentage for stochastic gradient descent algorithm is 75.19%. Based on the analysis, K-Nearest Neighbor (79.82%) performs better than stochastic gradient descent (75.19%).
Deep Learning Approach for Multi-Object Detection Using Yolo Algorithm

The fundamental task of object detection in computer vision has applications in many fields, including robots, surveillance systems, and autonomous vehicles. The You Only Look Once (YOLO) method has attracted substantial attention because of its real-time performance and high accuracy since the emergence of deep learning, which has revolutionized object detection techniques. This research study demonstrates a deep learning method for multi-object detection. The study assesses YOLO's performance in identifying multiple objects in cluttered and complicated settings and compares it to other cutting-edge object detection techniques. The effectiveness and efficiency of the suggested approach in attaining precise and real-time multi-object identification are demonstrated by experimental findings. This study also investigates the effects of various YOLO algorithm versions on multi-object identification performance, revealing the advantages and disadvantages of each. The results demonstrate the promise of deep learning-based methods, particularly the YOLO algorithm, for reliable and effective multi-object recognition in practical settings.
Unveiling the Language of Comics: Classifying Speech Balloons for Enhanced Analysis and Interpretation

This study introduces a new method for classifying speech balloons in comic images, utilizing a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) algorithms. The primary objective of the proposed model is to accurately distinguish between various classes of speech balloons. The evaluation of the model yields promising results, indicating its efficacy in achieving high precision, recall, and F1 scores across most speech balloon classes. Precision values range from 89.00% to 96.64%, denoting accurate positive predictions. Similarly, recall values range from 88.89% to 95.57%, showcasing the model's capability to effectively capture a substantial portion of positive instances in the majority of classes. The F1 scores, ranging from 89.14% to 96.10%, highlight the model's ability to strike a favorable balance between precision and recall. Additionally, the support values, which provide insights into the dataset's distribution by reflecting the number of instances per class, shed light on its characteristics. The model's accuracy values, ranging from 0.97 to 0.99, demonstrate its overall correctness in classifying speech balloons with a high level of precision across all classes. This research contributes to the field of image classification and offers valuable insights for the analysis and understanding of comic images.