Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Machine Learning and Blockchain

Abstract: This paper proposes a secure fraud detection model that combines machine learning algorithms and blockchain technology. According to the study it notes that while blockchain is considered a secure method of integration into finance, fraud and anomalies are still common in the network. To address this, the we use two machine learning algorithms, XGboost and KMeans classifier, to classify transactions based on patterns of fraudulent and legitimate transactions .The transaction data is also classified by supervised machine learning algorithms like Random forests and Decision Tree Classifier . The proposed model integrates these algorithms with blockchain technology to detect fraudulent transactions in the Etherium network . The paper includes a security analysis of the proposed smart contract and an attacker model to protect the system from potential attacks and vulnerabilities. The precision and AUC of the models are also calculated to measure the accuracy of the system. Overall, the paper proposes an innovative approach to addressing fraud and anomalies in the network using machine learning and blockchain technology. The integration of these technologies has the potential to improve the security of online transactions and e-banking systems.
Image Detection and Real Time Object Detection

Abstract: Object detection is a crucial task in computer vision with various practical applications, including surveillance, autonomous vehicles, and robotics. The YOLO (You Only Look Once) algorithm is a popular real-time object detection algorithm that has gained significant attention due to its high accuracy and speed. This algorithm processes the entire image at once and predicts bounding boxes and class probabilities for identified objects, making it ideal for time-sensitive applications. YOLO has evolved through various versions, with YOLOv5 being the latest and most advanced version that employs a feature pyramid network (FPN) and anchor boxes to improve its object detection accuracy. In this project, we aim to implement YOLOv5 for real-time object detection and image detection tasks. We will train the model on a suitable dataset and evaluate its performance on various benchmarks, comparing it with other advanced object detection algorithms. The project's outcome will provide a robust and efficient solution for real-time object detection that can aid quick decision-making in identifying object categories and their respective positions. It has practical applications in surveillance, automated driving, and robotics.
Breast Cancer Detection Using Machine Learning

Abstract: Breast cancer is one of the most contagious illnesses and the second leading cause of cancer mortality in women. Early breast cancer detection improves survival rates because better care may be given. Machine learning-based data categorization has been widely employed in breast cancer diagnosis and early detection. This literature review's primary focus is the categorization of accessible data using ML for breast cancer early pin-pointing and spotting. It is clear from reading multiple publications on artificial intelligence that there are several ways for detecting cancer. This study aims to compile reviews and technical publications on breast cancer diagnosis and prognosis. It provides an overview of the current research being done on various breast cancer datasets utilising data mining approaches to improve breast cancer detection and prognosis
Survey on Government Fund Allocation Using Blockchain

Abstract: Governments have a lot of responsibilities, which involves a lot of money transactions for various projects, employees, and farmer schemes. Corruption is a big problem as it's hard to track and can slow progress. We can solve this by using blockchain technology. Blockchain is a list of records that are linked and secured using cryptography. Each record contains transaction data, a timestamp, and a cryptographic hash of the previous record. We use security algorithms like AES encryption and decryption to protect the data. This project proposes a system to track government funds using key pair generation, metadata file decryption, and data verification algorithms.
Moodle platform and Zoom videoconference: learning skills in the virtual modality

In Peruvian Universities, most of the Moodle platforms and the Zoom videoconference were used in virtual teaching-learning. The teachers and students of the University of Sciences and Humanities, which is the object of study, used these tools in the class session. Moodle and Zoom complement each other for virtual classes during the pandemic time. The objective of the research is the influence of Zoom and Moodle on learning skills. The methodological design of the research has correlational scope, quantitative, non-experimental approach of cross-section. A survey was conducted of 95 students from the first cycles of the university. The instrument was validated by expert judgments with 86.3% approval. In addition, reliability was performed with Cronbach's Alpha, obtaining the value of 0.875. A p-value of 0000 was obtained as a result and the degree of correlation between Moodle and learning skills was 0.438** and Zoom with learning skills was 0.519**; determining in this way that there is a moderate positive significant correlation. It is concluded that the development of learning skills that correspond to digital skills, the communicative and cognitive part is due to the use of the Moodle platform and the Zoom videoconference in the learning sessions.
Implementation of image file security using the advanced encryption standard method

The application of technology in this era has entered digitalization and is modern. Therefore, we are already in an era of advanced and rapid technological development. It has become a human need to exchange information in every activity. Documents that contain information that is frequently sought or used. The document's use also includes essential information. Document security is undoubtedly a significant factor in prioritizing important information in a document to prevent unauthorized people from misusing the document's vital information. Cryptography is a method of overcoming document security issues so that third parties cannot read the information or messages contained within the document. The 128-bit advanced encryption standard (AES) algorithm is one of the algorithms included in the cryptography technique. Additionally, it can be combined with operation modes such as electronic codebook (ECB) and cipher block chaining (CBC) to create an application that can generate random codes to improve the security of the data contained in the document.
Architectural framework and register-transfer level design synthesis for cost-effective smart eyewear

In today’s time more than 70% of the world’s population suffer from eye disnormalities leading to the usage of eyewear or spectacles. Integrating profound technologies with daily utilities could serve some of the issues improving and optimizing our lifestyle to the most. One such way is to infuse nanosized chip in eyewear i.e., powered spectacles or shades to detect the location of the spectacles whenever it is necessary. The nanosized chip proposed has features including self-designed Bluetooth operating digital circuit, timer logic, clock generation using astable multivibrator circuit, emergency button, beep alarm and impact sensor. The values of resistance and capacitace is calculated to be 18 K ohm and 47 uF to obtain 1 Hz frequency. An optimal pin placement arrangement is analyzed, and the timing waveform is simulated using Verilog as proof of logical working of the chip. 13 D flipflops have been calculated to refrain from eye related strains. This paper suggests a bottom-up approach and develops the architectural framework of the chip, its working flow, system on chip top-view, digital logic description of each block and its implementation using Verilog hardware description language (HDL). The complexity and computational cost of the designed chip is minimal thus being commercially viable.
Content-based image retrieval using integrated dual deep convolutional neural network

The image retrieval focuses on finding images that are similar from a dataset that is of a large scale against an image of a query. Earlier, different hand feature descriptor designs are researched based on cues that are visual such as their shape, colour, and texture. used to represent these images. Although, deep learning technologies have widely been applied as an alternative to designing engineering that is dominant for over a decade. The features are automatically learnt through the data. This research work proposes integrated dual deepconvolutional neural networks (IDD-CNN), IDD-CNN comprises two distinctive CNN, first CNN exploits the features and further custom CNN is designed for exploiting the custom features. Moreover, a novel directed graph is designed that comprises the two blocks i.e., learning block and memory block which helps in finding the similarity among images; since this research considers the large dataset, an optimal strategy is introduced for compact features. Moreover, IDD-CNN is evaluated considering the two distinctive benchmark datasets of Paris and the oxford dataset considering metrics; also, image retrieval and re-ranking is carried out against the given query. Comparative analysis of various difficulty levels against the different CNN models suggests that IDD-CNN simply outperforms the existing model.
Information security investment prioritization using best-worst method for small and medium enterprises

In recent years, cyber security has become an increasingly important aspect of the decision-making process of corporations. It is essential to make investments in cybersecurity at this point to guard against the frequent disruption of business operations posed by cyberattacks. In the context of small and medium-sized organizations, this study recommends using a multicriteria decision-making technique to evaluate information security aspects. The information security index is derived as the foundation for every one of these features. The best-worst method is carried out to establish the best and worst possible security investments. In order to validate these findings, a survey was distributed to a variety of professionals and business decisionmakers. The criteria for selection are laid forth in the form of categories labeled technology and organization, respectively. The findings are presented in a rating system with three tiers, with the highest level representing the absolute best of the results. In the final section of the study, we will examine potential future possibilities for research and policy.
Nested approach for X-ray image enhancement based on contrast adaptive using histogram equalization

Medical image enhancement is a topic of great interest to researchers due to the rapid evolution of technology and advancements in communication. There are many types of medical images such as X-ray images, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, ultrasound images, positron emission tomography (PET) scans, single photon emission computed tomography (SPECT) scans, digital radiography images, mammography images and Fluoroscopy images. X-ray imaging is a valuable tool for diagnosis, monitoring, and treatment of many medical conditions, and its non-invasive, widely available, low cost and fast nature makes it a popular choice for many medical professionals. The proposed approach presents an algorithm for enhancing X-ray images, improving their visual appearance and making their content more useful and meaningful. The results of the algorithm show that enhanced images have a more natural look and provide accurate details of the objects in the X-ray images. Overall, this algorithm can aid in the diagnostic process by providing clearer and more detailed images for medical professionals to interpret.
The effectiveness of the Hermite wavelet discrete filter technique in modify a convolutional neural network for person identification

Classification is of great importance in the field of image processing, and convolutional neural networks (CNNs) have achieved great success in this field. Although CNN has proven to be a powerful technology for image recognition problems, it has failed in complex situations involving many realworld applications (for example, visual monitoring and automated driver assistance). Where it is difficult to detect a human in a series of images for various reasons. One of these reasons is the difference in the size of the human body, the height of the platform to which the camera is attached during the task of capturing accurate images, and the short training time in using the cameras, all of which are important factors to consider for the robustness and effectiveness of the human classification system. In this paper, a new deep CNN-based learning model is designed based on a new discrete waveform transformation (DWT) derived from discrete Hermit wavelet transform (DHWT) instead of modular wavelet, and the second stage is to train the convolutional neural network Hermit wavelets (HWCNN) is the most accurate and efficient deep learning.
Segmentation approach for offline handwritten Kannada scripts

India has more than 1,600 official languages, making it a multilingual country. Kannada, one of the major languages, originated in the state of Karnataka and is currently ranked 33rd among the accents that are most often spoken throughout the world. However, the survey shows that much more effort is needed to create a complete handwritten identification system. Segmentation is one of the crucial steps in a handwriting identification system that extracts significant objects from an image. The feature extraction and classification phases of handwritten text recognition will be more successful if the segmentation approaches selected are efficient. In the proposed system, segmentation was accomplished using bounding box and contour tracing methods. The result got is delivered to the next step of handwritten identification system. An average accuracy of 92.6% is worked out for line segmentation and word segmentation.
Development of natural language processing on morphologybased Minangkabau language stemming algorithm

Minangkabau language (ML) is one of the daily communication tools used by the people of West Sumatra, Indonesia. ML is a challenge in communicating. The ML language translation process is necessary to facilitate communication. This study aims to build a translation system for ML into Indonesian by developing the concept of natural language processing (NLP). NLP development adopts the performance of morphology-based Minangkabau language stemming algorithm (MLSA) which can separate basic words with affixes and endings. The research dataset adopts 600 basic ML words sourced from the big Minangkabau dictionary. The results of this study provide analytic output that can translate ML into Indonesian well. These results are presented based on the testing process on basic word input with an accuracy rate of 97.16% and based on text documents of 91.65%. Thus, the MLSA performance process presents the accuracy of the translation process. Based on these results, this research contributes to developing a stemming algorithm model in carrying out the process of removing prefixes, inserts, and suffixes in the Minangkabau language. Overall, this research can be useful as a tool for translating the ML into Indonesian.
An improvised coral reef optimization-based image registration using modified mutual information method

Computer visualisation and medical applications require image registration. It includes transforming collective picture data into the familiar coordinate scheme. Metaheuristic-based methods were developed to explain the issue and improve the efficiency and accuracy of conventional image registration techniques due to their limitations. We describe a hybrid medical image registration technique using bio-inspired meta-heuristic algorithms: Betteroffspring and multi-crossover strategies increase convergent time and solution quality with an improvised coral reef optimization with modified mutual information (ICOR-MMI) algorithm. This new optimization method (ICORMMI) proposes that coral reefs expand, compete for space, and reproduce. A linear weighted sum of image intensity and contour flow model intensity is added to the mutual information calculation. Including statistical and spatial image data improves image registration. The established technique has been tested and verified using multiple medical image data sets, some of which contain single-modality and mixed-modality images (CT, MRI). The registration validates the proposed model's accuracy and efficiency and shows posture's contribution by incorporating statistical and geographical image data. This strategy is adapted to the real-coding problem and tested for realtime issues. The ICOR-MMI algorithm-based hybrid approach outperforms current results in time efficiency and toughness.
A survey on blockchain for intelligent governmental applications

Blockchain technology has attracted a lot of attention lately because it is seen as an all-purpose method for conducting online transactions between unidentified parties without the need for a centralized authority. Modern developers and businesspeople think it will disrupt or even change both the government and industry. In this setting, government-focused blockchains are becoming increasingly prevalent. Several recent studies highlighted use cases in the public sector for decentralized information infrastructures. Blockchain is expected to revolutionize or at least simplify many governmental functions. However, because blockchain use has yet to be widely adopted in government or industrial settings, the question arises: what are the technical hurdles impeding blockchain adoption? To that purpose, a systematic literature assessment of 29 academic articles investigating software engineering problems in blockchain technology for government applications has been done. The papers are initially inductively analyzed in order to illustrate and identify the issues frequently highlighted in academic literature. Furthermore, a theoretical framework is discussed by relying on models used in traditional software development, which is then followed by deductive analysis to map out the blockchain use cases and future trends connected to the difficulties.
Real Time Human Body Posture Analysis Using Deep Learning

Abstract: We present a novel approach for accurately estimating the pose of objects in a low-cost and resource-efficient manner, making it suitable for deployment on embedded systems. Our algorithm comprises of two primary stages: object detection and spatial reconstruction. In the first stage, we employ a Convolutional Neural Network (CNN) called PoseNet for object detection. This approach has proven to be effective in detecting and localizing objects in an image. Next, utilizing stereo correspondences, we 3D reconstruct the spatial coordinates of multiple ORB features within the object's bounding box. This enables us to accurately estimate the position of the object in space. To calculate the final position of the object, we compute a weighted average of the stereo-corresponded key points' spatial coordinates. The weights are proportional to the level of ORB stereo matching, which enables us to obtain a more accurate estimate of the object's position in space. Our algorithm was tested in a calibrated environment, and we compared the results with a deep learning-based method using various datasets. The results show that our approach outperforms existing methods in terms of accuracy, while maintaining a low cost and efficient resource utilization. Our proposed method has several applications, including the quantitative and qualitative analysis of human posture. By analyzing all aspects of a person's posture, we can determine if there are any postural deviations, imbalances, or muscle weaknesses that may be causing pain or discomfort. This information can then be used to develop personalized rehabilitation programs, reducing the risk of injury and enhancing athletic performance. Furthermore, our approach can be used in various assistive technology applications, such as the control of robotic arms for pick-andplace tasks. The low-cost and resource-efficient nature of our algorithm make it ideal for deployment in embedded systems, enabling us to develop affordable and accessible assistive technology solutions. In conclusion, our proposed algorithm provides an accurate, low-cost, and resource-efficient solution for pose estimation, with a wide range of potential applications in human posture analysis, assistive technology, and beyond.
Design and Implementation of Virtual Try-On System Using Machine Learning

Abstract: Virtual try-on systems have become increasingly popular in the e-commerce industry, allowingcustomers to virtually try on clothes and accessories before making a purchase. However, current virtual fitting methods often suffer from pixel disruption and low resolution, leading to unrealistic try-on images. To solve this problem, we propose a Parser Free Appearance Flow Network (PFAFN) methodology that generates try-on images by simultaneously warping clothes and generating segmentation maps while exchanging information. Our experimental results show that PFAFN outperforms existing methods at a resolution of 192 x 256. The proposed virtual try-on system was implemented using Python and TensorFlow. The system's testing and validation were alsodiscussed. Our research contributes to the development of more realistic virtual try-on systems that could enhance customer experience and satisfaction in the e-commerce industry