Reviewerly/exHarmony huggingface dataset:
An Analytical Review of VM Allocation and Migration Policies in Cloud Computing

In a cloud data center, there is a large number of servers hosting the Virtual Machine (VM)s. The hosting and placement of VM is a big issue as data centers do not fully utilize the resources and thus, wastage of energy causes a considerable amount of operational cost and impacts badly on the environment. Therefore, VM placement plays important in addressing this issue. In the past, several techniques and algorithms were implemented for the efficient utilization of resources in terms of CPU utilization, energy consumption, Service Level Agreement (SLA) violation, and a number of VM migrations. This paper presented a review of VM placement considering these objectives. The review mainly focused on highlighting the state of art techniques with special attention on VM placement into the Physical Machines (PM)s from authenticated articles published from 2017 to 2022. The outcomes of the study showed that the challenge of VM placement has been addressed with the integration of various metaheuristic methods and machine learning algorithms in an energy aware manner. This is also summarized in terms of various metrics used for the analysis by the research community.
Digit Recognition using the Artificial Neural Network

Digit recognition using the Artificial Neural Network method is discussed in this study. Due to the enormous volumes of data and algorithms, the neural network can now be used to train the network and get the desired result. With the advancement in information and communication technology, internet access has increased as the use of technology increases the demand for digit recognition systems has gained popularity. This paper will discuss one of the techniques for digit recognition. We will train our model with the MNIST dataset & then test our model. Programming in Python is used to perform digit recognition. We have taken a dataset of 28,000-digit images, that will be used for training and 14,000-digit images for testing. The test performance accuracy of our multi-layer artificial neural network is 99.59 %.
MediBlock: A Blockchain-based Architecture for Secure Healthcare System

The existing healthcare domain has numerous components such as sharing of private medical records, medical insurance claims, and exchange of medical products among untrusted stakeholders viz. users (patients, doctors, nurses, healthcare staff, hospitals), and agencies (life insurance companies, pharmaceutical companies). However, the data exchange between different stakeholders of healthcare suffers from issues viz. fraud in the medical supply chain, dependency for coordination and collaboration among its stakeholders, traceability, reliability, security, and privacy of data. To achieve privacy& security of data as well as profitability & trust for all its stakeholders, Blockchain technology in healthcare is an appropriate alternative that proffers a complete scheme that records and shares essential data securely. The encrypted data generated by Blockchain technology becomes immutable. Hence, this research is intended to propose a Blockchain-based mechanism viz. MediBlock addresses the issues related to the healthcare domain by connecting various stakeholders. We further discuss the implications and potential for the actual adoption of MediBlock.
Supervised machine learning model builder for regression and classification analysis featured as a web application

A web-based tool called the machine learning model builder is created to create different machine learning models. For the most part, these models use regression or classification to address practical issues. Systems that have the same code hardcoded across all of them tend to be static. Dynamic file acceptance and understanding of the necessary visualizations and results are used to resolve this issue. The paper discusses a machine learning model builder that can quickly generate code and do repetitive tasks. This tool aids in the creation of a rigorous model. Various extensions have been added to the system to process rich datasets for the development of intelligent models. The text also addresses regression and other classification models as the mathematical aspect becomes more involved. Support Vector Machine, K-Nearest Neighbor, along with various Classification algorithms including Decision Tree Classifier and Random Forest Classifier; Ensemble learning approaches such as the XG-Boost classifier are all explored in detail below. Users can create training and validation sets as well as test the models they have created. The Python-based framework Django and the foundations of machine learning were used in the architecture’s creation. With little effort, backend systems can create, install, monitor, and manage the infrastructure. To help consumers better understand machine learning and its capabilities as a web application, this paper takes a user-centered approach.
Fast Prototyping of Trans-Medium Vehicle Fuselage with a T-splines Based Parametric Method

In the early development stage of a hybrid air-water vehicle concept, commonly known as trans-medium vehicle (TMV), requirements from air-cruise aerodynamic, underwater hydrodynamic and water takeoff and landing performance have to be considered in a coordinated and efficient way. Though basic requirements from the three aspects are reasonably well understood, it is a significant challenge to combine the specific features from these three configurations into the design of one vehicle. Current efforts on TMV primarily focused on validating relatively simple configurations without detailed effort to achieve the optimal configuration for this unique category of vehicles. This paper proposes to combine the fast shaping capability provided by T-spline tools with parametric design to offer a fast prototyping framework. The framework extends from fast prototyping and quick analysis to manufacturing a scale fuselage model using 3D printing. This framework is used to evolve a novel TMV concept, providing the basis for further, more detailed analysis, implementation and tests.
Deep Learning Based Face Mask Detection Model For COVID-19 Prevention

After the epidemic spread around the globe, particularly in underdeveloped nations in poor countries, the World Health organization (WHO) deemed the Novel Corona Virus (Covid-19) to be a dangerous virus in order to protect social security. People should limit their contact with other people, wash their hands often, and wear masks since there are few antiviral treatments and healthcare resources. As part of the safety procedures, masks are worn. At airports, offices, shopping centres, hospitals, and other public locations around the globe, there are COVID-enforcement police present in every nation. Under these circumstances, doctors and other health professionals are unable to influence patients’ health situations. Identification of the wearer’s face mask is a more effective method of preventing infection than human monitoring. Python, deep learning, and computer vision have all been integrated into this work effectively with Keras/OpenCV mask detector. Examining the outcomes of the system in comparison to several mask detection approaches.
A Medical Diagnosis and Treatment Recommendation Chatbot using MLP

A chatbot is an interactive AI that replicates human-like communication with the users via text message on chat without the intervention of humans. Its prior task is to help the users by providing answers to their questions. Chatbots are used in various fields. This Chatbot application is related to healthcare. It chats with the user who is suffering from any illness. During chatting with the user, the chatbot will identify the user's symptoms through natural language processing(NLP). It uses neural networks which use MLP(ANN) model to predict the disease and suggests the required medication to the user. Although It can not replace doctors, it provides useful diagnoses to the patient. It also saves users money and time by reducing doctor appointments to some extent. It will provide the diagnosis info of all the diseases present in the dataset. There are similar kinds of chatbots in existence that provides disease diagnosis services. But they are not giving the required medication services to the predicted diseases. They also don't ask any further questions to the user to really understand the disease. But this chatbot will overcome those limitations and will be very efficient in diagnosing diseases.
Segmentation of Handwritten University Examination Scripts

The proposed work intends to segment margin and page based on the answers of the handwritten university answer scripts, which further helps in the automatic evaluation of answers based on the respective question number. The documents are segmented based on the contents present inside and outside the margin of the handwritten answer scripts. The dataset was manually collected from various universities. The method of research proceeds with image pre-processing, binarization, and segmentation. The proposed work deals with the bounding box detection which when applied undergoes block segmentation where bounding boxes are formed across each answer block. The system when tested with the respective ground truth images generated an accuracy of 94%. Further the contents inside each bounding box are segmented, extracted, and stored in the directory. As a future work, the segmentedimages obtained can be further used to develop an auto evaluation system.
Privacy Preserving Data Mining and its Applications: A Survey of Recent Developments

Numerous services, including those in the fields of health care, finance, cyber security, business, and transportation, have benefited from Data Mining. However, a lot of the data may contain sensitive personal information, which presents privacy risks. A lot of privacy preservation methods have been introduced in recent times. These include clustering, k-anonymity, classification, association rule, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L$</tex> -diverse, distributed privacy preservation and so on. This research study focuses on the recent developments done in this field and presents a brief outline of the considered research works.
Web Application Security Testing Framework using Flask

This research presents a novel framework for automated web application security scanning and information gathering using the Axiom methodology. The framework assists organizations and security researchers in identifying and mitigating vulnerabilities in web applications by automating the discovery of publicly available assets and filtering targets based on initial responses, open ports and other criteria. The use of the Axiom methodology allows for faster scanning times and distributed scanning, making it useful for bug bounties and penetration testing. The framework's performance, limitations and challenges are evaluated. The research demonstrates the potential of the framework in improving the efficiency and scalability of web application security while emphasizing the need for proper configuration and ethical considerations.
A Survey on Machine Learning Techniques to Detect Breast Cancer

One of the diseases that most commonly affect women worldwide is breast cancer. In 2015, 8.8 million people lost their lives to cancer. Currently, in terms of incidence, lung cancer is second only to breast cancer among women worldwide. Cancer patients' lives can be saved through early detection and diagnosis. Therefore, it's critical to identify the tumor as soon as feasible. This survey covered several studies that were utilized to pinpoint this breast cancer as well as the methods they employed. This research study also discusses about the accuracy of different strategies and the datasets that were used and finally gives an overview of machine learning methods for recognizing and categorizing breast cancer. Research has been conducted on a variety of techniques, including Support Vector Machines (SVMs), Logistic Regression, K-Nearest Neighbors (KNN), Random Forest, Decision Tree, Neural Network, Decision Support System, K-Fold Cross Validation, etc. Finally, all the techniques are compared based on their merits and shortcomings. This research study aims to gather and assess different breast cancer detection methods.
Design of an Elliptic Curve Cryptography Encrypted Blockchain-based Electoral System

Every person has the fundamental right to vote in a democracy, which gives them the opportunity to choose the country's future leaders. It gives citizens of the community the chance to voice their opinions. It helps them comprehend the importance of citizenship. Elections and voting are carried out securely using online voting tools. This system establishes a digital voting platform by removing paper ballots and in-person voting. Additionally, the proposed digital system forbids voters from using more than one ballot and protects the legitimacy. Blockchain based Electronic Voting (E-Voting) system is proposed to ensure security, privacy and transparency.
Revolutionizing Anomaly Detection in Surveillance Footage with ARLSTM

Video surveillance is an essential tool in maintaining public safety, security, and protecting assets. However, monitoring surveillance footage continuously for extended periods is challenging, and even the most vigilant human operators can miss anomalies or unusual activities that could pose a threat. Automatic Schemes are required to identify abnormalities in surveillance footage. Anomaly detection in surveillance footage is a thought-provoking problematic due to the high dimensionality and complexity of the data. Newly, deep learning-based methods have exposed promising results in detecting anomalies in video streams. Specifically, Long Short-Term Memory (LSTM) networks have been extensively used for 0mold temporal dependencies in video data. This paper proposes a novel approach for effective anomaly detection in surveillance footage using Attention Residual Long Short-Term Memory (ARLSTM) networks. The proposed ARLSTM architecture integrates attention mechanisms, residual connections, and LSTMs to improve the detection accuracy of anomalies in surveillance footage. The attention mechanism enables the network to selectively focus on relevant features in the data, while residual connections facilitate the propagation of gradients and alleviate the vanishing gradient problem. Furthermore, the LSTM component allows the network to capture temporal dependencies and model long-term patterns in the data. Investigational outcomes on standard datasets demonstrate that the proposed ARLSTM approach outperforms existing state-of-the-art methods for irregularity discovery in investigation footage. The proposed approach has potential applications in various domains, including public safety, security, and industrial automation. The proposed ARLSTM approach offers a robust and effective solution for detecting anomalies in surveillance footage. This work demonstrates the potential of deep learning-based approaches for automatic video surveillance and overlays the technique for future research in this field.
Recevent: NLP based Event Recommender System

Due to the constantly rising number of physical events that occur in an individual's surroundings today, there is a need for a system that recommends relevant events to the individual based on user location, interests and history, making the individual's choices easier. Hence, this research study intends to provide a solution to this problem of the Event-selection dilemma by creating an application capable of suggesting events on the same aspects. The system makes use of a Hybrid Recommendation Algorithm - combining the advantages of NLP technology and Content-based Filtering algorithms. The system also uses automated web scraping techniques for mass aggregation of events from verified sources and related data like venue, genre, price and description. This research study reviews various NLP, Deep Learning, Mathematical algorithms and techniques to understand recent advancements in the field of recommendation frameworks and proposes Recevent: An event-recommending web application.
Peningkatan Hasil Belajar Matematika melalui Metode Penemuan Terbimbing dengan Setting Kooperatif

This research is a class action research (Classroom Action Research) which aims to improve students' mathematics learning outcomes through the guided discovery method with a cooperative setting. The subjects of this study were students of class IX at MTSSMuhammadiyah Enrekang. This research was conducted in two cycles, each of which had three learning process meetings and one meeting where the learning outcomes test was conducted. The data collection technique used in this research is data on learning outcomes taken by giving tests at the end of each cycle, while data on teacher activities and student activities are taken using observation sheets during the learning process in the classroom. Based on the results of research and discussion for two cycles, it can be concluded that students' mathematics learning outcomes have increased from cycle I to cycle II. These conclusions were obtained from: (1) The average increase in students' mathematics learning outcomes from cycle I was 68.11 to cycle II of 83.11. (2) The students' mathematics learning mastery increased from cycle I to cycle II, namely from 22 students increased to 35 students, and classically achieved 100% completeness. (3) Increased student activity in participating in learning from cycle I to cycle II.
Promoting first graders’ scientific thinking through picture books with the 5E model

Picture book reading is essential in the early stages of learning and development. The current study aimed to incorporate picture books to improve first graders’ scientific thinking, including scientific reasoning and creativity. The study adopted a quasi-experimental research design. There were 24 students in the experimental group and 23 in the control group. The chi-square test was used to analyze the difference between the two groups. The teacher innovatively employed the 5E model strategies to aid students in the experimental group focus on the scientific knowledge and methods proposed in the picture books. The results revealed that students in the experimental group outperformed their counterparts in scientific thinking with small to large effect sizes. Besides, according to students’ attitudes and the types of books borrowed, picture book teaching with the 5E model was appropriate for first graders to improve their scientific thinking.
Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030.
The Criteria for Adapting a Blockchain Consensus Algorithm to IoT Networks

The Internet of Things (IoT) is a network of smart objects connected via the Internet, where each object is identified by a unique, accessible, and programmable address, while Blockchain is a technology for storing and transmitting information based on a decentralized system such that the centralized validation of transactions is replaced by a consensus mechanism. The integration of these two technologies requires consensus protocols to overcome the major challenges encountered in IoT systems, including issues related to security, storage capacity limits, computing power, etc. Therefore, different consensus algorithms have been implemented. For this purpose, this paper presents a research study on the criteria that would render a Blockchain consensus algorithm suitable for IoT networks, namely, computing power, latency, storage capacity, and security.
Reservoir based spiking models for univariate Time Series Classification

A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficient-they run mainly on the power hungry CPUs and GPUs. Computing with Spiking Networks, on the other hand, has shown to be energy efficient on specialized neuromorphic hardware, e.g., Loihi, TrueNorth, SpiNNaker, etc. In this work, we present two architectures of spiking models, inspired from the theory of Reservoir Computing and Legendre Memory Units, for the Time Series Classification (TSC) task. Our first spiking architecture is closer to the general Reservoir Computing architecture and we successfully deploy it on Loihi; the second spiking architecture differs from the first by the inclusion of non-linearity in the readout layer. Our second model (trained with Surrogate Gradient Descent method) shows that non-linear decoding of the linearly extracted temporal features through spiking neurons not only achieves promising results, but also offers low computation-overhead by significantly reducing the number of neurons compared to the popular LSM based models-more than 40x reduction with respect to the recent spiking model we compare with. We experiment on five TSC datasets and achieve new SoTA spiking results (-as much as 28.607% accuracy improvement on one of the datasets), thereby showing the potential of our models to address the TSC tasks in a green energy-efficient manner. In addition, we also do energy profiling and comparison on Loihi and CPU to support our claims.
How to effectively use distributed ledger technology in supply chain management?

Distributed Ledger Technology (DLT) refers to multi-party systems that enables saving encrypted data across a peer-to-peer network of nodes, without central authority. While DLT applications have been mainly studied in finance, we conduct empirical research on DLT application in supply chain management, combining theory testing and theory elaborating case research. Applying the Theory of Affordance Actualisation, we identify five DLT affordances: (1) verify product origin and history, (2) exchange data on digital product models, (3) track and trace products’ logistics, (4) simplify supply chain finance, and (5) automate payments. We identify and evaluate these affordances and also outline how these affordances can be actualised. We contribute to the discourse of DLT value creation and provide practical guidance to assess DLT potential in supply chains. We integrate our findings into the academic discussion on collaboration in viable, intertwined supply networks.