Reviewerly/exHarmony huggingface dataset:
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem

A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning’s efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study’s discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications.
Crypto Logistic Network: Food Supply Chain and Micro Investment using Blockchain

The contemporary food supply chain has significant gaps between involved stakeholders in aspects like transparency in production and distribution and quality of the material. Monitoring the product movements starting from the raw material production phase can help enhance seamless transparency and traceability of the product between the producers, stakeholders, and also customers.A blockchain based network in the food supply chain can help maintain transparency with it’s ledger of transactions and allows all the stakeholders to know the movement of the products as they have access to every transaction made in the network. Additionally, farmers economical situation has been drastically decreasing which is mainly due to attributes like bank debts, landlord interferences and poor yield of crops. Integrating the concept of micro investments with blockchain-based food supply networks can help mitigate the bank debts of farmers and promote sustainability. This study considers the potential of blockchain in domains like food supply chain and agriculture along with attempts to create a solution for societal needs.
Hand Gesture Recognition using Convolutional Neural Networks

The sign language detection detects the hand signs made by the physically challenged to people understandable language. This is done by creating dataset on our own and training those images using the CNN algorithm. By doing this, the physically challenged people are benefitted as this bridges the gap between the signers and the non-signers. The project helps to convert the sign language to the human understandable language. The images give us the output in English words. The words are shown in real time as the person shows his/her hands under the camera. Usage of webcam is required for this project to run successfully. This also decreases the stress of hearing impaired people.
Language-focused Deepfake Detection Using Phonemes, Mouth Movements, and Video Features

The potential implications of Artificial Intelligence (AI) and Deep Learning (DL) algorithms in generating highly realistic deepfake videos have raised concerns regarding the reliability of our human senses. In response to this challenge, we propose a deepfake detection system based on phonemes, the transcribed text, associated mouth movements, and video-extracted features. As a proof-of-concept, we develop a deepfake detection system specifically designed for the Portuguese language, employing three presidential candidates from the 2022 Brazilian elections. Additionally, we introduce a unique dataset comprising real and fake videos involving these three individuals and deliberately blending their identities. The extracted features consolidate relevant attributes, which we utilized to train multiple classification algorithms. Notably, our computational models demonstrate satisfactory performance when authenticating or detecting fake videos containing at least one of the trained phonemes from the Portuguese language. Hence, we conclude that deepfake detection is feasible, primarily due to the absence of natural expressions, particularly in non-English language deepfake videos. Furthermore, developing individual-guided deepfake detection systems may facilitate the authentication of videos featuring celebrities or politicians during future online events.
A Multi-objective Genetic Approach to Clustering with Solution Recommendation

Clustering, which attempts to partition data points into different clusters according to their features, is the most commonly used analysis method in data mining. Prototype-based clustering algorithms are widely used because of their simplicity and efficiency, but it is less effective for non-spherical distributions. Evolutionary algorithms are increasingly applied to improve the quality of partitioning solutions. However, firstly the existing prototype-based evolutionary clustering algorithms still have difficulty in giving unsupervised recommended solutions for complex distributions. Secondly, evolutionary clustering algorithms with complicated optimization process reduce the efficiency of the underlying clustering algorithm. Both of them seriously affect the general applicability of the prototype-based evolutionary clustering algorithms. In this paper, a novel prototype-based evolutionary clustering algorithm is proposed, which is valid for arbitrarily shaped datasets. In the proposed algorithm, a solution recommendation scheme that does not require a priori knowledge is developed and applied to preserve solution diversity and recommend a single solution. What’s more, a new cluster merging process is applied to promote population quality and accelerate convergence. To verify the effectiveness, experiments were conducted on different aspects of the proposed method using several artificial and real datasets. The results indicate that our algorithm is able to obtain suitable recommended solutions for spherical and non-spherical datasets and outperforms related clustering algorithms.
Research on Improved Grey Wolf Optimization Algorithm Based on Random Walk in Task Scheduling of Cloud Computing Platform

With the development and widespread application of cloud computing platforms, efficient task scheduling has become the key to improving the performance and resource utilization of cloud computing systems. In order to improve the efficiency of task scheduling on cloud computing platforms, a simulation framework for task scheduling on cloud computing platforms is constructed, and the Grey Wolf optimization (GWO) algorithm is improved, combined with the Random Walk (RW) algorithm to optimize task scheduling on cloud computing platforms. The simulation results show that the maximum completion time of the research method is lower than that of GWO algorithm and RW algorithm. During the task completion process, the RWGWO algorithm has the lowest execution cost and the highest virtual machine resource utilization efficiency. When the number of virtual machines is 4, RWGWO has an average improvement of 12.33% and 5.12% compared to RW algorithm and GWO algorithm, respectively. The research method not only reduces the maximum completion time, but also reduces costs, which can effectively save cloud resources.
Trajectory-based Metaheuristics for Improving Sparse Matrix Storage

Kernels in linear algebra are memory-bounded routines and their performance is dependent on the sparsity pattern of the matrix operands. Since memory is many times slower than arithmetic operations these kernels tend to exploit small fractions of the peak performance of modern architectures such as GPUs. In this sense, the improvement of the storage of the matrices to reduce the memory accesses is a main line of work.The problem of finding an exact solution for the best permutation of a matrix is computationally prohibitive thus it is interesting to explore metaheuristic approaches. Previous work found good results with evolutionary algorithms but with high execution time. In this context, it is compelling to explore trajectory-based metaheuristics as a way to reduce execution time since they require evaluating only one solution per iteration. In this work, we continue the efforts and present heuristics based on VNS and ILS which outperform the evolutionary algorithm in the majority of the instances evaluating half of the solutions.
Handwritten Digit Recognition Using CNN with Average Pooling and Global Average Pooling

Recognition of handwritten digits is crucial because it enables computers to comprehend and understand human handwriting. The automatic sorting of mail, processing of financial records, and reading of prescriptions are only a few examples of its many practical uses. It can also be used in the classroom for exam grading. The automating process with handwritten digit recognition eliminates mistakes and saves time. The major goal of this paper is to examine several existing model configurations and give trustworthy and effective ways for detecting transcribed numerical data. This paper highlights Convolutional Neural Network (CCN) with Average Pooling (AP) and Global Average Pooling (GAP) layer for proper extraction of insights from the feature maps. Results indicate that the proposed Convolutional Neural Network classifier with AP and GAP layer outperformed existing neural network with significantly increased computational efficiency without sacrificing execution. This paper development is based on the Modified National Institute of Standards and Technology, or MNIST dataset, which is a well-known handwritten digits dataset in the machine learning and computer vision fields. Each handwritten digit is a 28x28 grayscale image, and there are 70,000 of them in total numbers from 0 to 9. Thus, the classification model is of classes 10. 60,000 images are there in the training dataset and 10,000 images are there in the testing dataset. The proposed CNN Model with AP and GAP Layer achieves a classification accuracy of 99% on the MNIST Dataset.
Gender Recognition using Deep Learning Methodology

Identifying a person’s gender from their facial features is a prominent subject in computer vision. While humans can intuitively perform this task, it poses substantial challenges for machines. The research study associated with this article details a gender classification method anchored in face recognition feature vectors. Initially, input images undergo face recognition and preprocessing, leading to a standardized face format. Subsequently, the data is processed by the face recognition model to extract feature vectors, encapsulating facial attributes within a specific feature domain. Ultimately, machine learning techniques are deployed to classify these derived vectors. This research introduces a sophisticated gender classification system harnessing VGG Face, Deep Belief Networks, and shifted filter responses. Models such as CNN, VGG 16, Resnet50, Inception v3, and EfficientNet were explored, with Resnet 152 emerging as the most effective. Remarkably, the Resnet 152 models exhibit a 9% enhancement over leading competitors, and they also demonstrate superior resilience to anomalies compared to previous iterations.
Improving Imbalanced Data Classification Through Integrated Data-Level and Algorithm-Level Techniques

Imbalanced class distribution is common issue in machine learning and data mining. It affects various applications like fraud detection, medical diagnosis, and network intrusion detection. The mentioned problem occurs when the majority class greatly outnumbers the minority class, hindering learning systems designed for balanced distribution. Furthermore, presence of overlapping instances between two classes exacerbates problem. This paper introduces a novel method that integrates ensemble and cost-sensitive techniques at algorithm-level with OSBNR approach at data-level to address this challenge. Furtherly, suggested technique is compared with conventional resampling methods, in conjunction with three ensemble algorithms and three cost-sensitive algorithms, to determine optimal combination. Simulation presents OSBNR is having optimal results in terms of processing time and evaluation metrics like G-mean, AUC-ROC, TPR-Recall, precision, Fl-score and FPR. PyOSBNR is simulated as python index package which can easily handle class imbalance in the presence of behavioral noise.
Automatic classification of categories for Great Hobbies Inc. based on Vendor Information

This paper focuses on developing a product code classification system for Great Hobbies, a retail company specializing in the sale of hobby products. The system was built to provide a quick and efficient way to categorize the large number of products that the company offers. The pairing of products with the category is done by humans taking into account structured and unstructured data. Examples of unstructured data are name and product descriptions; structured data consist of the vendor’s product code. There are over 200 different combinations of department and product codes in Great Hobbies, which makes this pairing a time-consuming process. A machine learning-based approach was used to build the classification models. Multiple models were developed and evaluated based on their accuracy and time complexity. The best model was selected and deployed as a component of a decision support software system that runs on the Shopware platform. The system was designed with three main components: a front-end application, a web API, and backend workers. Results from the implementation show that the models have achieved a high degree of accuracy in classifying product codes into their respective categories.
CNN classification subject to practical aspects of Search and Rescue UAV missions

The use of Unmanned Aerial Vehicles (UAVs)equipped with classification systems in Search and Rescue missions has increasingly shown great application potential. There are however pratical concerns regarding limited embedded computing power and adapting the classifier performance to the characteristics of a specific mission. The UAVs may acquire a large number of images during a mission and it is difficult for humans to search throughout this material. For this matter, we study the performance of a leaner Convolutional Neural Network model to select images with salient objects and present a tool that can help in decision making regarding the definition of the classification threshold through the analysis of possible operational impacts and consequences of eventual detection errors. We also show that, in this application, Data Augmentation is important to improve the classifier performance.
Decentralized Cloud Disaster Recovery using Consensus Algorithm

As enterprises rely more on cloud infrastructure, the requirement for reliable disaster recovery solutions becomes critical. This study investigates a novel way to disaster recovery by utilizing decentralized cloud networks and consensus algorithms and also addresses resource allocation, security, and efficiency concerns in the context of existing cloud disaster recovery solutions. Research shows how decentralized networks may improve data resilience, minimize recovery times, and optimize resource consumption. It illustrates the benefits of this new method through empirical assessments, giving a radical viewpoint on cloud disaster recovery.
Deep Neural Networks-based Malignant Breast Lesions Detection and Segmentation from Mammography

Breast cancer has the highest incidence among all cancer types for women globally. Convolutional Neural Networks (CNN) is the most widely used models for computer-aided breast cancer detection in mammograms. This paper used CNN-based architecture for malignant lesion detection and segmentation in mammography images using the public CBIS-DDSM and an in-house (IMI) dataset. The CBIS-DDSM consists of 1000 images, and the IMI consists of 295 images. The CBIS-DDSM was randomly split into 80/10/10% of training/validation/ testing datasets. In this study, we applied the U-Net model and devised two setups: first, train and test the model with the CBIS-DDSM, and second, use transfer learning in which the pre-trained model on the CBIS-DDSM dataset is fine-tuned by the IMI train set and tested on the IMI test set. Evaluation metrics for calculating the similarity include the Dice coefficient, but we also reported the Intersection-Over-Union index. The Free-Response receiver operating characteristic curve (FROC) was chosen to evaluate the detection performance as an evaluation criterion. The dice and IOU values of the first setup were 0.98±0.006 and 0.97±003, respectively, which indicates that the predicted lesions are highly similar to ground-truth lesions. The FROC curve of setup1 shows that the model could detect lesions with the true positive rate of 0.99±0.17 at the false positives per image of 2.7. In setup 2, the pre-trained model with the CBISM-DDSM dataset tuned with the IMI achieved the dice and IOU of 0.92±0.0 and 0.98±0.008, respectively, on the IMI test set. Also, the model detected lesions with a true positive rate of 0.98±0.20 at the false positives per image of 3.3. Our study showed the potential of the Transfer learning techniques to guarantee the reliable performance of publicly developed models by tuning them with a low number of in-house patients.
A Novel Study on Data Science for Data Security and Data Integrity with Enhanced Heuristic Scheduling in Cloud

Recently, “cloud computing” has become an attractive paradigm for the administration and provisioning of internet-based services. This paradigm is known as “cloud computing.” The promise of this being a reality has been around for a while, but cloud computing has risen to meet the shifting conditions of the IT industry. In this article, we introduce the trust model, the suffrage heuristic, the Heterogeneous Earliest Finish Time (HEFT) scheduling algorithm, and the trust value computation. The trust model has parameters that vary with values of sub parameters and the tasks they perform. These functions are regarded as a standard of durability and cannot be broken. The assumption that better mappings can be made by allocating a resource or machine whose expected time of completion will suffer if that machine is not allocated is the foundation around which a suffrage heuristic will be built. Due to its great performance and stability across a variety of graph topologies, the HEFT technique is a promising solution for the Directed Acyclic Graph (DAG) and associated scheduling problem on the heterogeneous systems. The average schedule length is longer in the suffrage without trust scenario compared to the HEFT without trust, suffrage with trust, and the HEFT with trust scenarios. It is possible that there is no algorithm that, given a scheduling problem, can produce a best solution in polynomial time. Utilising random variables to find solutions to scheduling issues calls for the application of a stochastic approach as well. In addition to swarm algorithms and evolutionary algorithms, these strategies made use of Simulated Annealing (SA). The initial goal of the effort was to use a GA, and then the Firefly Algorithm (FA), to optimise the cost and performance of workflow scheduling. Algorithms like the HEFT and the GA have been proposed in this work. For applications utilising a finite set of heterogeneous processors, this HEFT serves as the underlying algorithm. We can use the GAs to find a global optimum for issues with a large and irregular search space.
Textual Mining using Deep Learning in official journals

The Brazilian official journals contain relevant government information and official acts are only considered valid after their publication. The content of the journal is made up of unstructured data and in large volumes, thus difficulting the access and extraction of knowledge. Some tools are specialized in filtering, organizing and label the information found in these journals, but still have errors in their filtering. This work proposes the development of Textual Mining architectures with the use of pre-processing techniques and pre-trained natural language representation models in Portuguese that use the bidirectional encoder representation of transformers for the binary classification of errors in content filtering of Brazilian official journals. The results of the models were quite efficient, considering that the best model presented a result of 95.05% of accuracy.
All Action Updates for Reinforcement Learning with Costly Features

Machine learning algorithms are broadly applied in classification tasks. Based on available information and the respective label, a model learns the patterns and generalizes to new, unseen information, predicting its label. In some problems, there is a cost related to acquiring the information needed. This sets up a scenario where not only classifying with high performance is desired but also classifying with low cost, trading off between performance and cost. Different real-world problems can fall into this scenario, e.g. in medical diagnosis where information is given by exams that have a monetary cost, or text classification when only a limited amount of words can be used by the model. These can be called Budgeted Machine Learning problems. In this context, this paper provides a Machine Learning algorithm based on Deep Q-Networks to solve a specific Budgeted Machine Learning problem named Classification with Costly Features, where the data comes from a tabular data set and each feature has an attached cost. The provided algorithm is dynamic with respect to the feature acquisition, and classifies once enough information is gathered. The proposed algorithm fully updates the Q-values target vector at the same time step, leveraging available information about the data set, in a supervised fashion. Tests on six different data sets were carried out. The proposed algorithm showed competitive results with the proposed baseline.
A Reversible Approach for Hiding Data in Digital Media using Gray-Scale Images

Inevitably, Secret data is communicated through digital media using traditional data-concealing techniques. The known approaches like data hiding in the literature cannot restore the original media. Only the Reversible Data Hiding (RDH) method allows for the simultaneous recovery of the original storage media and the concealed information. In cases where the original media must be preserved, RDH can be employed. As a result, RDH is employed in numerous contexts where the original medium is required alongside the concealed information. To conceal sensitive information in an initial image pixel, Difference Expansion (DE) based RDH analyzes the difference between surrounding pixels. The secret information in the HbS-based technique is inserted into the histogram’s peak and zero bins. To embed the secret data into the original medium, the prediction error (PE) is enlarged in the PEE-based approach. RDH’s primary goal is to insert additional data with little loss of quality. In this study, we first investigate three methods for hiding information within the primary cover image and then retrieving it. Later, we’ll look at how the PSNR of these three systems compares to one another. Finally, the research has drawn a conclusion based on the three methods for incorporating data with minimal loss of quality.