Reviewerly/exHarmony huggingface dataset:
Neuro-Vascular Mapping of Junctions in Human Brain from MRI Scans using Image Segmentation

Image segmentation represents a fundamental technique widely employed in diverse fields, with particular prominence in the biomedical domain for extracting intricate and crucial entities from images. Within the scope of this research, we have harnessed the potent capabilities of the U-Net architecture to perform image segmentation specifically on junctions discernible within neurovascular MRI images of the human brain. This undertaking serves as a crucial stepping stone towards the overarching goal of constructing a comprehensive and finely labeled 3D graph representation of the neurovascular system. The motivation behind developing a neurovascular mapping of the human brain through Convolutional Neural Networks (CNNs) lies in the potential insights we can glean concerning the intricate dynamics of blood flow within this complex organ. By scrutinizing the nodes and connections of this neurovascular mapping, we gain a more profound understanding of the hemodynamic processes, thus paving the way for subsequent investigations into diffusion modeling. The diffusion model derived from these hemodynamic insights holds promise for early stroke detection by identifying regions of insufficient blood flow before the onset of clinical symptoms. In the course of our current study, albeit conducted with a limited set of training examples, we have determined that the InceptionV3 architecture, when employed as the backbone for U-Net-based segmentation, emerges as the optimal choice. This selection is rooted in rigorous empirical analysis and serves as a vital component in our pursuit of advancing the accuracy and effectiveness of neurovascular image segmentation for improved stroke detection and neurovascular system mapping.
Research on Rough Scene Matching Navigation Method Based on Semantic Bag of Words Model

This article presents a method for rough scene matching navigation method using semantic information under satellite denial conditions. In the task of scene matching navigation, the current methods still rely on algorithms that require the detection of point features or other geometric features in the images. These algorithms have high requirements for image illumination, color, and angles, leading to weak scene adaptability. In this paper, we first partition the reference image and use YOLOv8 to extract semantic features from each part and encode them to construct a semantic feature library for the reference image. We then detect and encode the semantic information from the images captured during actual flight trajectories. By comparing the semantic features of the captured images with those in the feature library, we obtain rough matching blocks. The experiments were conducted using images captured by drones and benchmark maps extracted from high-altitude remote sensing satellites. The results show that this method proposed has good matching speed, reliability, and scene adaptability, providing aerial vehicles with rapid rough positioning and navigation capabilities.
Density Dedicated Deep Learning Model for Mammogram Malignancy Classification

A body of literature has reported the promising performance of deep learning models when applied to mammograms for different clinical tasks. However, a major pitfall of deep learning models is when they are applied to mammograms bearing high-density breasts since the overlapping high-dense tissue can cover the lesion and make diagnosing and interpreting it difficult. Thus, analysis of dedicated models trained for low- and high-dense breasts can be of great importance. In this study, we aimed to develop low- and high-density dedicated deep-learning models for classifying breast masses on mammograms into benign and malignant cases. Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) dataset, including mammograms, cropped to masses, and the pathologic diagnoses, were adopted for this study. For dedicated models, the dataset was split into low-(BI-RADS density1 and 2) and high-dense (BI-RADS density3 and 4) groups. Contrast-limited adaptive histogram equalization (CLAHE) was applied to enhance the contrast of the images, and then all images were resized to 255 × 255 matrix size and normalized. A modified DenseNet-201 deep neural network was trained with the learning rate starting at 0.0001 and decreased in a piecewise manner every epoch with the RMSProp optimizer. Fifteen percent of training data was excluded for validation, and training was continued for 100 epochs. Data augmentation, including rotation, flipping, and scaling, was implemented on the training dataset to prevent overfitting. The trained model was evaluated using the test set. Accuracy (ACC), area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) for the general model were 0.720, 0.771, 0.732, 0.701, for the Low-density model were 0.788, 0.818, 0.824, and 0.742, and for High-density model was 0.712, 0.621, 0.962, and 0.180, respectively. Our study highlights the importance of developing dedicated models tailored to the nature of high-density breasts to improve overall accuracy. We also suggest performing specific preprocessing for high-density breasts, such as region-wise contrast enhancement for the regions with high-intensity values (with a gamma filter, etc.).
Analysis of Traffic Management System using YOLOv8

The identification of traffic violations plays a pivotal role in contemporary efforts to manage traffic effectively and enhance safety on the roads. Traffic violation detection holds immense significance due to its profound influence on road safety, traffic control, and the overall welfare of communities. It plays a pivotal role in molding cities that are both sustainable and adaptable, ultimately guaranteeing an enhanced quality of life for both their inhabitants and guests. The proposed model elevates traffic enforcement and surveillance through its capability to ascertain the volume of vehicles traversing a road simultaneously by affixing a label denoting the vehicle’s count and its category. Furthermore, it discerns instances of vehicles breaching red traffic signals, meticulously logging the vehicle’s count number, thereby streamlining the process of accessing comprehensive vehicle information. Our model detects the multiple vehicles at a time which violates the rules and yields 97% classification accuracy.
Enhancing Facial Recognition and Tracking for Security Applications using Haar Cascade Classifier

Facial recognition is used in biometric technology. Facial recognition is the process of using the face to recognize or verify a person's identity. It captures, analyses, and compares patterns based on a person's facial details. The task of authenticating or identifying human faces from multimedia photos is accomplished using facial recognition technology. Viola and Jones present a method capable of accurately and quickly detecting faces in images. This technology can be used to accurately detect the facial features. Face tracking technology provides better opportunities for security and surveillance. This increases the level of protection. For example, facial tracking software helps improve surveillance strategies and serves as a basis for identifying terrorists and criminals. National borders, casinos, museums, banks or prisons are other examples where this system can be used for security. This paper proposes a face detection and eye and smile recognition system with real time images, using the Haar Cascade classifier for face, eye and smile recognition. This trace is based on the jpeg/jpg file format and can be acquired from any type of camera. It works with almost all types of image formats. Its classifier will detect facial features. In this research paper a comparison is also analyzed between face and facial detection by a camera captured image and saved images. Analysis of both types of code is also being made in this research work. The document also gives the percentage of accuracy by comparing the results. Therefore, this work aims at face processing with eye and smile detection. In this article it first detects the face using a web camera and then the captured image is processed and, on the image, it detects the face, eyes and smile.
Unveiling Subpopulations and Patterns within the Pima Indians Diabetes Dataset through FCM Clustering Analysis

This research study analyzes the application of Fuzzy C-Means (FCM) clustering to the Pima Indians Diabetes dataset, which is commonly used in the field of medical data analysis for diabetes outcome prediction. FCM is a robust clustering algorithm designed to identify inherent patterns and structures within complex datasets. The primary aim of this research was to uncover potential groupings or clusters within the dataset based on key features associated with diabetes risk factors. The dataset was loaded from a local file and underwent meticulous preprocessing to ensure that all data values were numeric. Specifically, non-numeric values were transformed into NaN (Not a Number), and rows containing missing values were omitted from the analysis. This preprocessing step was essential to prevent errors stemming from non-numeric data during the clustering process. Following data preparation, the FCM clustering algorithm was employed. The FCM algorithm assigns data points to clusters based on their degree of membership in each cluster. The number of clusters and the fuzziness parameter were set, and stopping criteria were established to control the clustering procedure. The results of the FCM clustering were visualized to assess the formation of clusters within the dataset. The clustering outcomes were displayed using a scatter plot, where each data point represented a specific entry and was color-coded to indicate its cluster membership. Cluster centers were depicted as red X markers. The application of FCM clustering to the Pima Indians Diabetes dataset demonstrated its capability to reveal latent structures within medical data. The identification of subpopulations or clusters within this dataset holds the potential to enhance diabetes interventions and treatments by tailoring them to specific patient profiles. It is important to note that while FCM clustering provides insights into data patterns, further analysis and domain knowledge are necessary to comprehend the clinical relevance of the identified clusters within the context of diabetes management and diagnosis.
Comprehensive Survey on Human Motion Tracking System

Human motion tracking systems have gained significant attention in various industries, including healthcare, sports, entertainment, and robotics, due to their potential to revolutionize technology interaction and monitoring human movement. This review explores the evolution of motion tracking technology, including optical, inertial, and depth-based systems, and the use of machine learning and computer vision techniques to enhance accuracy. The review covers applications in healthcare, sports training, virtual reality, gaming, and human-robot interaction. Understanding the current state of human motion tracking is crucial for the development of technology-driven solutions.
A Comparison of Cloud Security Mechanisms

Cloud computing is a broad term that can be understood differently depending on the context. Its primary goal is to reduce capital expenses on infrastructure, operating systems, deployment, and storage. The National Institute of Standards and Technology (NIS T) defines cloud computing as on-demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing operates using virtualization and parallel computing principles and provides services like IaaS, PaaS, SaaS, and NaaS. The cloud is classified into public, private, hybrid, and community clouds, each with its own unique features. The major issue of cloud computing is to transfer the data with security breaches. Various security systems are available to protect the data security in the cloud. This research study has discussed some security models like Advanced encryption standard algorithm, Biometric model, Elliptic-curve cryptography with Diffie-Hellman algorithm and a new technique vector-matrix based encryption algorithm. The execution time for these algorithms were compared and results were discussed.
Applications of Blockchain Technology for Secure Transaction of Electronic Health Records

The healthcare industry is not immune to the trend of adopting new IT systems, and this includes hospitals and doctors’ offices. To store a patient’s medical history (including diagnoses and treatments), medical history (including diagnostic pictures), and financial data (including demographics and insurance details), a computerised database known as an “Electronic Health Record” (EHR) is used. Because medical records are typically private and must be shielded from prying eyes, ensuring their secure transfer is a top priority for healthcare systems. Electronic health records (EHRs) are used in the proposed study to ensure the security of all sent medical data, including the Chest X-ray MedPix images. This article describes a blockchain-based solution that patients can use to keep all their data in one secure location. This system was built with the Ganache tool on the Ethereum network, making use of Solidity and web3.js as well as other languages, tools, and approaches. In this study, a systematic method is described wherein blockchain smart contracts are used to record patient information and execute tasks in a distributed environment. After a smart contract has been deployed, all transactional communication occurs within its encrypted confines, protecting both the parties and their personal information. As an added bonus, the validated and broadcasted changes intended by the transaction can be checked. To ensure that government agencies have quick access to protected records, there is a cryptocurrency wallet (MetaMask) that stores the information in an encrypted, decentralised manner. Both patients and physicians can use the wallet to log in. In addition, the system will manage and protect all of the doctor and patient’s information. Things like the following are in scope for the proposed system: Users may now access the same information at the same time because to blockchain’s distributed ledger capabilities, which boosts productivity, establishes trust, and lowers barriers to entry. Setting individual permissions for users allows for safe data storage. In addition, the suggested method enables the safe transmission of patient medical records.
FPGA-based Low-Power Encryption using the Elliptic Curve Digital Signature Algorithm

The proposed work gives a solution to the increasing demand for resource-constrained environments for low-power cryptographic solutions: field-programmable gate array (FPGA) implementation of the Elliptic Curve Digital Signature Algorithm (ECDSA) for low-power encryption. By leveraging the parallel processing capabilities of FPGA, the suggested approach effectively mitigates power consumption while maintaining security. The study extensively analyzes the hardware’s architecture, design elements, and energy-conserving strategies explicitly developed for FPGA implementation. The results indicate that the ECDSA-FPGA system exhibits superior processing speed and energy efficiency compared to conventional encryption methods. This contribution elucidates the imperative nature of striking a harmonious equilibrium between energy efficiency and security in contemporary cryptographic systems, thereby bolstering the progress towards secure communication in the Internet of Things and wearable technologies.
Enhancing Traffic Surveillance and Urban Mobility with Vision-Based Vehicle Analysis

Covering a spectrum of approaches from traditional methods to cutting-edge deep learning models, this comprehensive examination delves into the latest advancements in vehicle detection, recognition, and tracking through vision-based methodologies, offering a thorough analysis of their strengths, limitations, and realworld applications. Beginning with the evolution of vehicle detection methods, it explores traditional techniques like edge-based methods and motion segmentation approaches such as frame differencing, background subtraction, and optical flow, showcasing their efficacy in identifying moving vehicles and their historical significance in early traffic surveillance systems. Emphasizing the transformative impact of Convolutional Neural Networks (CNNs), particularly in challenging scenarios marked by occlusion and varying lighting conditions, the paper details how CNNs have revolutionized vehicle detection accuracy. Beyond detection, it examines vehicle recognition and classification techniques such as colour recognition, license plate recognition, logo recognition, and vehicle type classification, highlighting their practical applications in traffic analysis, security, and urban planning. The exploration extends to vehicle tracking strategies, encompassing model -based, shape -based, and feature based approaches, with a comparative analysis of tracking algorithms based on factors like accuracy, computational efficiency, and adaptability. This comprehensive research offers valuable insights for researchers, practitioners, and policymakers aiming to advance traffic surveillance systems and shape the future of urban mobility through vision-based vehicle analysis.
Optimizing Medical Image Report Generation with Varied Attention Mechanisms

Image captioning plays a crucial role in interpreting visual information in the medical field. This research paper represents an innovative approach to produce descriptive and insightful reports for its respective medical images. The combination of natural language processing and computer vision techniques are used, an encoder-decoder architecture is employed which aids in leveraging by using a qualified InceptionV3 model to extract image features. The captions which are generated were based on a deep learning model with a Gated Recurrent Unit-based decoder and an attention mechanism, guaranteeing the connection between the generated text to the image content. The research focuses mainly on the dataset which consists of medical images with its associated reports by utilizing data extraction and preprocessing methods to enhance the accuracy and contextual understanding of the generated report. The experiments and results section demonstrate the implementation details and efficacy of the proposed model in generating meaningful report for the medical image, thereby facilitating enhanced interpretation and analysis of medical images. This research contributes to the advancement of image captioning techniques in the medical domain and holds significance for clinical decision support systems and medical image analysis.
Biometrics - Based Voting Machine Combined with Result Analysis Software

India is a growing nation where the electronic voting system is still in danger as a result of election cheating. Elections are a key factor in determining the country's future in the next five years. Hence the current voting system has to be more secure because the present system lacks individual identification then there is a possibility that someone may abuse another person's voting privileges. In this study, fingerprint- based voting authentication is suggested to prevent voting abuse. Here we have included software that is used to update the status of the voting count during the elections. Using Biometrics any individual can vote in any election booth near their residential areas.
Computer Cursor Movement with Eye Blink for Paralyzed Individuals

This research study addresses the challenges faced by individuals afflicted with quadriplegia, who often struggle to operate a conventional mouse. Leveraging cutting-edge technology, the system employs Canny-detection, and Face-Mesh-Facial recognition techniques to implement electronic eye movement control for paralyzed individuals. With approximately 40 cases per millions of quadriplegia reported annually, the need for innovative solutions is evident. By accurately identifying and analyzing data points such as pupil shape and size, the system enables seamless navigation of the computer interface. The implementation, executed through PYTHON serial commands in CMD, eliminates the dependency on external assistance, significantly simplifying the lives of those with limb paralysis. This pioneering approach not only offers practical convenience but also underscores the transformative potential of technology in enhancing accessibility and independence for individuals facing physical challenges.
Detecting Counterfeit Goods with a Blockchain- Enabled Barcode System

In the quickly changing world of technology, there is always a danger that a technology or product may be developed globally. Examples of this risk include copying and counterfeiting, which can hurt a company’s brand, revenue, and consumer health., are examples of the rapidly expanding world of technology. The project’s primary objective is to consider whether the customer’s purchased item matches the description in the false or real compared to block chain, we have the traditional supply chain. Standard supply chains provide compact networks where the company delivering the service or the goods retains the data. Because they have such control over the market and the data, they are susceptible because they can alter it anyway they see fit. For genuine businesses, counterfeiters present serious problems, but far too many people are unaware of the full extent of the impact that these products have on brands. Various strategies have been developed in the past to circumvent the issue of product counterfeiting. Anti-counterfeiting strategies frequently used include those based on QR codes, AI, and RFID tags. These techniques do have certain limits, though. For instance, it is simple to copy QR codes and add them to fake goods, which reduces their usefulness. Convolutional neural networks (CNNs) and machine learning are two examples of AI technologies that demand a lot of computational power, which may be challenging and resource-intensive. Due to these challenges, further research and development are required to solve them and improve the efficacy of anti-counterfeiting techniques. The goal of this initiative is to increase forged goods identification using supply chain history tracking. This is accomplished using Throughout the supply chain, block chain technology enables the identification and tracking of genuine items. Everything is made decentralized and accessible by several parties at once via a block chain-based system. One of its key benefits is that the data is incredibly safe and protected from any vulnerabilities since it cannot be changed without the approval of all parties involved.
Tau PET Image Harmonization Using a Generative Adversarial Network

Multi-scanner image harmonization is a major challenge for longitudinal imaging studies that often span several decades and transition from legacy scanners to state-of-the-art alternatives. In this preliminary tau PET imaging study involving 5 human subjects sequentially scanned on a GE Discovery MI PET/CT scanner and a Siemens HR+ scanner, we train and validate a generative adversarial network (GAN) for harmonizing tau PET images between the two scanners. Our network receives as inputs an HR+ image, a high-resolution anatomical MR image, and spatial information. The method was validated on clinical data by comparing the synthetic images generated by the GAN with those generated by a convolutional neural network (CNN) and by deconvolution stabilized by a total variation penalty. Our results show that the GAN outperforms both classical deblurring and the CNN in terms of image quality and quantitation as indicated by the peak signal-to-noise-ratio.