Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Autonomous Maze-Solving Robot based on an Enhanced Maze Solving Techniques

In this article, the design of a self-governing robot that solves mazes integrating distance sensors is presented and wheel pivot encoders to navigate through unknown areas based on its decision-making capabilities. The robot efficiently solves mazes by following the shortest path and storing the past information for future reference. The primary application of this robot is in navigation, and the paper explores the maze-solving algorithms while highlighting the advancements in autonomous maze-solving systems. The robot is equipped with wall recognizing distance sensors. The robot aims to operate in environments inaccessible or challenging for humans and can be instrumental in achieving specific objectives. The proposed algorithm utilizes the flood-fill approach for maze-solving. The flood-fill method is employed by the suggested algorithm to solve mazes.it is more effective than the methods used now. There are possible uses for this approach in robotic path finding and tracking.
A Convolutional Neural Network Based Iterative Framework for Plant PET Image Denoising

Plant positron emission tomography (PET) images usually suffer from noise because of positron spillover effect. Hence, it is worth performing image denoising. In this paper, we propose a convolutional neural network (CNN) based iterative framework and unfold the CNN-based iterative framework into a deep network, equipped with an unsupervised learning strategy, namely regularized Noise2noise. The iterative framework mathematically solves the denoising problem by half quadratic splitting and gradient descent method, and benefits from the CNN capacity and flexibility of exploiting image characteristics. Furthermore, the regularized Noise2noise provides a constraint that considers the difference between the ground truth corresponding to the input and to the label. The iterative framework was tested on dynamic plant PET images and compare with three popular denoising methods. Experimental results show that the iterative framework is effective in boosting the denoising performance. The iterative framework achieves an average signals to noise ratio of 34.93 and an average contrast to noise ratio of 62.51, outperforming all compared methods.
Speech Recognition System for Upper-Limb Impairment: A Text Editor for Programming via Voice

Programming is one of the most important skills to learn for the present and future generations. However, those with hand disabilities may find it difficult to program with a traditional keyboard and mouse. Disabled people would greatly benefit from assistive technology that allows them to use their voices to control computers. The existing system recognizes user input as voice and translates it into a program. However, the existing system only recognizes a limited number of keywords in a programming language. It would also be difficult to debug errors if the voice was misinterpreted. Our suggested work in speech recognition using natural language processing (NLP) would perform better than the previously available systems. It is possible to build a foundation for programming without a keyboard by creating tools that use a publicly accessible speech recognition APL Voice control of the user interface for program execution and code debugging would also be quite beneficial. This can be done with the help of modules such as Pynput. Keyboard, PyAutoGUI, which can simulate mouse cursor movements and clicks as well as keyboard button presses. The accuracy of our proposed system is 87%. When converting voice commands to text, the Word Error Rate (WER) is 18%.
Automatic Segmentation of Prostate and its Anatomical Sub-regions from MR Images using a Novel ResUnet Model

Magnetic resonance imaging (MRI) provides detailed anatomical images of the prostate and its anatomical sub-regions for radiation treatment planning. Automated segmentation of the prostate and its sub-regions from MR images facilitates prostate cancer diagnostic and treatment planning. In addition, the identification of the whole prostate gland, the distinction between the central gland (CG) and the peripheral gland (PZ) would enable differentiate diagnosis since the risk of cancer varies in these zones. However, their boundaries are often very vague and blurred in anatomical images. This work will present a ResUnet model for accurate regional segmentation of the prostate zones from magnetic resonance (MR) images. The focus would be on the segmentation of the whole prostate gland (WG), central gland (CG), and peripheral zone (PZ) from T2-weighted MR images, where PZ+CG=WG. A novel ResUnet model was proposed and trained using 81 clinical datasets and evaluated on 35 subjects undergone T2-weighted MR imaging from 6 different centers. The performance of the proposed segmentation model was compared with the conventional Unet model with the same number of trainable parameters. The ResUnet model with ISBI13 dataset achieved the whole prostate, central zone, and peripheral zone delineation with maximum Dice indices of 89.93±1.0, 91.56±2.0, and 89.04±2.5, respectively. While the Unet model with the same dataset exhibited inferior performance compared to the proposed ResUnet model with maximum Dice indices of 88.75±1.1, 89.81±2.2, and 77.25±3.5 for the whole prostate, central zone, and peripheral zone, respectively. The quantitative analysis of the segmentation results exhibited the superior performance of the proposed ResUnet model over the conventional Unet model. This model could be employed for automated prostate (as well as its sub-regions) segmentation to reduce the workload in the diagnosis of prostate cancer.
Smart Presentation Control using Computer Vision

The use of slideshow mode in Power Point presentations today is a powerful and appealing method for speakers to persuade audiences and provide information. But since we need to use specific devices, this presentation might occasionally become less engaging. The current research has used hand gesture recognition technology which improves human-computer interaction, to make presentations more creative. This project focuses on the development of hand gesture detection systems with a versatile set of functionalities. These functionalities encompass not only traditional slide navigation but also innovative features like drawing, erasing, displaying a dynamic pointer, zooming in and out, and enabling rapid transitions between slides. Our primary focus is enhancing drawing accuracy by fine-tuning hand tracking and gesture recognition models for pinpoint precision, enabling effortless creation of intricate, error-free drawings. This application makes use of OpenCV or TensorFlow technologies for precise hand tracking and landmark detection. A camera with high resolution is employed in this system to recognize user input through gestures. Creating a system that can recognize human hand gestures and utilize that knowledge to control a presentation is the fundamental goal of hand gesture recognition.
A Study on YOLOv5 for Drone Detection with Google Colab Training

Drones are increasingly being used for various purposes, both commercial and recreational, but they can pose security threats when misused. Therefore, the need for a reliable and accurate drone detection system becomes essential. The aim of our project is to utilizes YOLOv5, a state-of-the-art object detection algorithm, which has demonstrated exceptional performance in detecting various objects and evaluate the accuracy of the drone detection system through extensive testing and presents the quantitative analysis of these metrics, demonstrating the effectiveness and reliability of the solutions. The output of the system is a visual representation of the detected drones overlaid on the video footage. This provides real-time feedback to the user, allowing for prompt response and decision-making. Collecting a diverse dataset with accurate annotations is a persistent challenge, requiring meticulous organization. Adapting YOLOv5 to recognize drones amidst various flying objects demands careful tuning and may be impacted by varying drone sizes and orientations. Integrating this complex model with Google Colab, while beneficial, requires managing computational resources effectively. Furthermore, optimizing the model’s accuracy involves challenges in balancing synthetic data augmentation for improved generalization without compromising real-world relevance. Despite these hurdles, the model showcases proficiency in real-time drone detection, emphasizing the effectiveness of the approach in addressing the complexities of aerial object recognition.
Genetic Algorithm Optimized BiLSTM Networks Model for Human Activity Recognition using Timeseries Sensor Data

In recent years, Human Activity Recognition (HAR) is a method used to classify human physical activities in a given period of timestamp based on accelerometer and gyroscope sensors collected data. It plays vital role in various applications like assistive living to monitor individuals’ behavior, training & monitoring employees and health & fitness monitoring with the use of various types of sensors present in mobile phones and wearable devices. This system is used to detect the abnormal behavior in assistive technology. Deep learning provides an opportunity to solve many issues in various sensor-based applications. In this work a hybrid method which combines the benefit of Genetic Algorithm (GA) and bidirectional LSTM is proposed. This machine learning (ML) model results in better accuracy up to 97.3 and 98.8% for UCI smartphone and WISDM public datasets respectively and robustness in recognizing real time activities.
Enhancing Cloud Services: Streamlining Performance Metrics and Cloudlet Scheduling

Enhancing cloud service is the primary goal of cloud stakeholders. It is a comprehensive goal that can be achieved through optimizing performance metrics, effective resource utilization, and prioritizing user satisfaction. These performance metrics, also called Quality of Service (QoS) metrics, are mentioned in the Service Level Agreement (SLA), a contractual document. Optimizing metrics, resource utilization, and user experience requires continuous monitoring systems and cloud technologies such as virtualization, scheduling, migration, consolidation, load balancing, etc. Scheduling cloudlets, virtual machines, and load balancing are crucial for achieving the SLA enumerated QoS and other key demands. In order to monitor the performance and evaluate the effectiveness of any cloud service knowledge on key performance metrics, scheduling and load balancing become imperative. This paper is orchestrated to identify essential performance metrics and explore how scheduling and load balancing algorithms enhance cloud performance. Additionally, it seeks to conduct a comparative evaluation of FCFS, SJF, Min-Min, Max-Min, RASA, Suffrage, and TASA cloudlet scheduling algorithms using CloudSim. This evaluation focuses on key scheduling metrics, including average waiting time, makespan, virtual machine utilization ratio, and load balancing.
A Comprehensive Review on Image Restoration Methods due to Salt and Pepper Noise

Digital images are well-use in various fields like satellite communication, mobile communication, medical and security. Visualized information helps the people to understand the things easily by seen. Improper capturing, age of camera lens, imperfect storage and transmission leads to introduce noise in the image. Gaussian noise, salt and pepper/impulse noise and speckle noise may affect the original image due to aforementioned reasons. Out of these, impulse noise/salt and pepper noise is one of the major types, degrades the image with black and white spots it results loss of required information. Hence, restoration of ground- truth image from such type of noisy image is a challenging task to provide quality and clarity visuals to users. Several linear and non-linear methods have been proposed by researchers since more than four decades. Non-linear methods based on; median filtering approach; adaptive median filter approach; median filter with switching condition; and median filter with rank order type; are proposed from early 1980’s onwards. All of these operated directly on pixels in spatial domain. Hence, they are very easy to implement and most of them are not that much robust at middle and higher noise density circumstances. Further, various researchers have been implemented linear methods such as wavelet transform methods like S WT and DWT. Majority of these are works well upto 50% noise density conditions and very few works well on higher and multiple noise density conditions also. To overcome these problems CNN’s based methods have been developed tremendously by various researchers from last decade and these methods require huge database to train the network model. Most of these, achieved good accuracy rates at higher and multiple noise conditions. Hence, here a detailed review report is presented on impulse noise removal methods with their Peak Signal to Noise Ratio (PSNR).
Automated Detection of Violence in Detached Areas using Hybrid Deep Learning Models: A YOLO-5 and CNN Approach

The human violence rate increases gradually every year and it’s increasing at a rate of 7% annually. It has been observed in many studies that violence takes place suddenly in detached areas. The major hurdle to stopping these acts is a delay in information exchange. To tackle these issues, automated detection techniques were employed. CCTV footage was employed to identify objects with computer vision terminology and actions were taken based on correctly identified objects. These CCTV cameras are extremely helpful to resolve such cases and take appropriate action. The violence and non-violent acts were identified based on the analysis of footage. Here, in this research, an effective and appropriate method was employed to detect the violent and non-violent acts from the CCTV footage. In this context, a deep learning model was proposed in the research that comprises two deep learning models especially YOLO-5 and Convolutional neural network (CNN). Here, initially, the images were preprocessed to enhance the quality of CCTV footage and afterward, YOLO was employed to identify objects from the available CCTV footage and later, the classification was done using CNN. The performance hybrid model was evaluated on different parameters and compared with other state-of-the-art techniques. After comparison, it has been observed that the proposed model outperformed with an accuracy rate of 98.63%.
Deep Reinforcement Learning Bearing Fault Diagnosis Method Based on Improved Reward Function

In deep reinforcement learning, the traditional reward function is limited in the problem of multi-class sample imbalance. In this paper, an improved reward function based deep reinforcement learning bearing fault diagnosis method is proposed. Firstly, the method improves the definition of reward function, and introduces a weak punishment mechanism to deal with the situation where the diagnosis action is wrong but the fault label is similar. Then, the samples are divided by K-means clustering method, and the reward value is newly defined according to the Euclidean distance from the cluster center and the proportion of sample labels. This method makes the diagnostic model explore and learn more effectively, and can deal with multi-class classification tasks effectively. The experimental results show that the method achieves remarkable performance improvement in multi-class classification tasks, and improves the accuracy and recall rate.
The Automatic Testing Method of Basic Platform Interface of Centralized Control System Based on Multi-agent Model

In order to solve the problem of lack of effective detection means and insufficient detection automation for the API interfaces of the basic platform, the API interface detection scheme of the basic platform of the centralized control system is formulated. The API interface detection system based on the multi-agent model is further built, adopting a design architecture that separates test case management and execution, preparing test cases according to the application software development requirements, which can completely cover the API interfaces of the basic platform under test, and standardize the usage and performance of the API interfaces of the system under test. It has effectively promoted the construction of an open ecological model of “platform + application” of a new generation of centralized monitoring system for substations.
RMSA-Net: A 4D Radar Based Multi-Scale Attention Network for 3D Object Detection

Perception is a fundamental task in autonomous driving systems(ADS) and robotic systems alike, which commonly uses multi-modal sensors including cameras, LiDAR, and radar. Recently, 4D radar has gained increasing attention thanks to its advantage of high angular resolution in both azimuth and elevation. As the unique features of 4D radar point clouds call for more efforts on brand new object detection algorithms based on 4D radar, there are few studies in this topic. Therefore, in this paper we fully consider the characteristics of the sparse and shapeless 4D radar point clouds and propose a model RMSA-Net to fuse rich contextual information and address the problem of height information loss. We conduct extensive experiments to demonstrate that our model achieves the gain in terms of 3D mAP and bird’s eye view(BEV) mAP by 3.28% and 0.96%, respectively.
Exploit Detection and Mitigation Technique of Cache Side-Channel Attacks using Artificial Intelligence

As a persistent danger to computer security, cache side-channel attacks take advantage of minute flaws in microarchitectures to access sensitive data without authorization. In-depth analysis of these attacks’ numerous forms and related Common Vulnerabilities and Exposures (CVEs) is provided in this study report. The study emphasizes cache side-channel attacks’ practical ramifications and how they affect system security. The methods for identifying these threats are examined, with an emphasis on how machine learning and deep learning approaches might be used to strengthen security precautions. The research offers useful insights via pseudo code and data structure figures that highlight the necessity of real-time monitoring and detection tactics in reducing cache side-channel vulnerabilities. Artificial intelligence is used to design proactive countermeasures. The research article calls for proactive security measures to thwart these developing risks in addition to illuminating the complicated landscape of cache side-channel attacks. The contribution to this domain goes beyond theoretical discussion and includes actual application. This paper demonstrates the critical relevance of real-time monitoring and detection tactics in the fight to defend systems from cache side-channel attacks via the presentation of pseudo code and data structure diagrams. The results of this work provide guidance for future research and highlight the crucial value of interdisciplinary cooperation in the continuous quest for safe computing systems.
Enhancing Shift-Invariance for Accurate Brain MRI Skull-Stripping using Adaptive Polyphase Pooling in Modified U-Net

Image segmentation is an essential aspect of image processing, where regions of interest (ROI) are identified by employing feature descriptors like edges, color, and texture. Medical images often contain unwanted segments, necessitating preprocessing steps to extract valuable information. In the realm of medical image segmentation, skull-stripping in 3D volumetric Brain MRI images holds significant importance. This study delves into supervised learning techniques to extract crucial brain features from non-brain tissues. The approach involves a modified U-net that operates on 2D slices of the brain MRI, yielding a skull-stripped image with superior accuracy compared to existing methods. The architecture incorporates Adaptive Polyphase Pooling layers, enhancing the architecture’s robustness and the model’s generalization. These Adaptive Polyphase Pooling layers ensure shift-invariance in the predicted output, maintaining consistency even if the single view of the Brain MRI shifts along the two axes in the plane.
Design of ADCF-based Online Learning Resource Recommendation System for Preschool Education

Online learning has emerged as a brand-new method of knowledge transfer as a result of the Internet and the field of education coming together due to the rapid development of information technology. The proliferation of online learning tools, particularly in the area of preschool education, gives pupils more learning opportunities but also creates issues with selection and learning effectiveness. The goal of this study is to develop an Attention-based Deep Collaborative Filtering (ADCF) preschool online learning resource recommendation system by combining the attention mechanism collaborative filtering algorithm and deep learning algorithm. This system will assist students in selecting the most appropriate online learning resources from a large pool of online learning course resources and increase learning effectiveness. The results show that the average scores of the experimental class’s curriculum paper, early childhood teaching activity design and kindergarten game creation were 94.58, 88.15 and 92.43, respectively, which were 6.10%, 7.63% and 1.97% higher compared with the control class’s average scores of 89.14, 81.90 and 90.64, respectively. This study has significant practical implications for enhancing the recommendation of online learning resources and raising the standard of preschool education, as can be seen in the conclusion.
Esha-256: An Enhanced Secure Cryptographic Hash Algorithm for Information Security

Due to the exponential growth of digital data the need for security and authenticity plays an important role in various applications. Hence an effective method is required to protect the data, one such method is Secure Hash Algorithm (SHA-256). The SHA-256 is a strong and secure algorithm providing authenticity and digital signature. Though SHA-256 has security, reliability and strong collision resistance features, it is vulnerable to Algebraic Fault Analysis (AFA) and Differential Fault Analysis (DFA) attacks. It also requires longer processing time for hash generation. In order to overcome the above mentioned drawbacks, this study proposes a method called Enhanced Secure Hash Algorithm (ESHA-256) with less computation time and more security. The proposed ESHA-256 has 32 rounds and modified compression function for hash generation. In this method, initially the 64 word vectors are reduced into 32 by applying XOR with adjacent word vectors and the resulting 32 word vectors are given as an input to all the rounds. The compression function is modified by incorporating the first 32-bit fractional segment of cube root of initial 64 primes as two parts. The experimental results of proposed ESHA-256 produces the hash with minimum time which is 53% less when compared existing SHA-256 and also highly resistance to AFA and DFA attacks.