Miracle-dz/UniFAR-FTUs huggingface dataset:
Digital Transformation, Corporate Governance and Enterprise Performance: Empirical Evidence from China

As part of this paper, we examine the impact of digital transformation and corporate governance on the performance of corporations by combining data from Shanghai and Shenzhen A-share companies listed between 2007 and 2020, evaluating corporate digital transformation and corporate governance separately through the entropy method, and using the moderating effect model for empirical analysis. The results show that the support of governance institutions can assist companies in improving their performance, while the development of corporate digital transformation has a positive impact on the productivity level of listed companies. However, it is found that the positive effect of digital transformation on corporate performance becomes weaker when the level of corporate governance is higher. Finally, a categorical test by non-state-owned and state-owned enterprises concludes that business digital transformation and corporate governance contribute significantly to driving impact on corporate performance both in state-owned and non-state-owned firms, but this driving effect has a greater impact on state-owned companies. The results provide insights for policymakers and regulators on the current practices of digital transformation and corporate governance and their potential economic impacts, which can be used to achieve a "coupled" development of digital transformation and corporate governance, reduce corporate costs, and better contribute to corporate performance.
Deep Learning and Internet of Things based Detection of Diseases and Prediction of Pesticides in Fruits

Agriculture plays a vital role in the Indian economy, being one of the largest employers and contributing significantly to the country's Gross Domestic Product (GDP). The prevalence of diseases in fruits can have a severe impact on crop yield, leading to financial losses for farmers and affecting the entire agriculture industry and economy. Early detection and prevention of diseases are critical to minimizing their impact on crop yield. An IoT and Deep Learning-based fruit disease detection system can provide a solution to this pressing issue. The proposed system automatically detects fruit diseases and suggests preventive measures to minimize their impact on the yield. This improves crop monitoring efficiency and saves time and resources that are required for manual monitoring. The use of deep learning algorithms in the system ensures high accuracy in disease detection and prevention, ultimately leading to improved crop production and increased financial benefits for farmers. This study discusses about the process of image classification using deep learning algorithms like modified CNN, ResNet50, and VGG19. In conclusion, the IoT-based fruit disease detection system using deep learning is a valuable tool for farmers and others in the agriculture industry to ensure optimal crop production and improve the overall economy.
Design of an Efficient Bioinspired Model for Optimizing Robotic Arm Movements via Ensemble Learning Operations

Robotic arm movements are highly dependent on design and deployment of sensors & actuation devices & their duty cycles. Optimizing current-level duty cycles for these devices can reduce the power consumption, and maximize the efficiency of control for different device operations. Existing duty cycle control models for robotic arms are highly complex, or have lower efficiency levels. To overcome these issues, this text proposes design of an efficient bioinspired model for optimizing robotic arm movements via ensemble learning operations. The arm is built using Arduino controller along with stepper motors, which assist in controlled movements for different arm operations. The proposed model uses Mayfly Optimization (MO) in order to identify duty cycles of different arm components for different movement types. The MO Model uses delay, energy and jitter parameters in order to estimate a fitness function that is optimized in order to identify arm movement sets. These movement sets are classified into performance-aware movements via a combination of Naïve Bayes (NB), k Nearest Neighbours (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multilayer Perceptron (MLP) classifiers. Due to which the model is able to reduce the delay needed for control the arms by 8.3%, reduce the energy needed for control operations by 2.9%, and reduce the control jitter by 4.5% under real-time scenarios.
Ensemble Common Features Technique for Lightweight Intrusion Detection in Industrial Control System

The integration of the Industrial Control System (ICS) with corporate intranets and the internet has exposed the previously isolated SCADA system to a wide range of cyber-attacks. Interestingly, the vulnerabilities in the Modbus protocol, with which the ICS communicates, make data obfuscation and communication between component entities less secure. In this work, we propose a Common Features Technique (CFT) for Lightweight Intrusion Detection based on an ensembled feature selection approach. Our Common Features Technique, which used fewer features, was able to detect intrusion at the same level as models using information gain, Chi-Squared, and Gini Index feature selection techniques datasets after fitting Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN) models. More importantly, when p-values were computed, the CFT model computation time and memory usage were statistically significantly different at 95% and 90% Confidence Interval (CI) when compared to the model on the other techniques.
Coordinated-axis Weight for Real-Time Trace Transform

This paper presents a method for building Coordinated-axis Weight (CW) that has the advantage can be used to increase the speed for converting image data into the domain of real-time trace transforms. The main idea for this method is to use a feature reduction method for image data that must be used in cooperation with supervised machine learning outcome predictions. But the obtained result still retains the distinctive features of the data image in the form of a trace transform. Start by reducing the number of features from the image data, which will be used to reduce the size of the image to the appropriate image size, which in this article will reduce the image size to 45x45 pixels. The next step is to train the data using a Decision Tree (DT) method by aligning the reduced image data in a 1D model. In addition to the image data, two more features are added, i.e. the $\phi$ and $\rho$ positions, to inform the prediction that each position has a different value. And the last step is to bring the data to create a model for predicting the answer. The weights obtained from the model are called Coordinated-axis Weights (CW). The experiments within this paper were experimented using the Caltech-256 database, which was able to measure the MSE, RMSE, and PNSR of the images compared to the original trace transform response images at 4.1, 2.02, and 42.00, respectively, including the highest predictive speed of 0.2 ms per image.
Neural Network-Based Software Vulnerability Classification To Enhance Accuracy

The security risks are increasing due to software flaws. The vulnerability dataset used in this design had taken from the cve web site. Additionally, the updated error description is matched as input, cross-referenced with the dataset and if a match is found, the error order is set up. It is now possible to manage software vulnerability using an automatic bracket system, and the system’s security performance will also improve. It will also reduce the chance of system damage and attack. In this design, a brand-new model called the automatic vulnerability bracket model built on bracket algorithms for Convolutional Neural Networks has been put out. 900 neuron input layers, 16 retired layers, and 27 affair neurons are used to build the model. Age range is adjusted, and it is delicate.
Optimizing the process of Police hotlines

the usefulness of the data entered by emergency hotlines (911,112) operators can be optimized by an automatic validation system for the quality and accuracy of this information and extracting additional data that may be useful for taking decision. For this, telephone exchanges can be transformed into text (using “Speech to Text”) to extract names of people or companies (aggressors, victims), places, type of crimes or offenses (using “Named Entity Recognition”). On the other hand, as this database grows, the need for intelligent search engine will be mandatory, for fast and intelligent inquiry of useful information. In this paper we did a comparative work, so firstly we focused on the part related to the classification of the Arabic written texts saved in the database of the system where we used different methods of transformation starting by TF-IDF(Term Frequency - Inverse Document Frequency) and word index Tokenizing passing to CBOW(Continuous Bag Of Words), DBOW(Distributed Bag Of Words) and Embedding, then we tested many suitable models as naive Bayesian models, deep neural networks with LSTM(Long Short Term Memory) and Word2vec concepts. At last we compared all to Transformers applying AraBERT(Arabic Bidirectional Encoder Representations for Transformers). And secondly, NER (Named Entity Recognition) model that classifies certain words/sequence of words in the text into the four entities (Suspect, Victim, Crime Location, Crime Date). This was accomplished by training and validating two machine learning algorithms for token classification (or named entity recognition), AraBERT being the model with the most significant results. This NER model was later tested on Arabic crime texts that we have scraped from Facebook, to examine its performance on new and unstructured Arabic data. Another goal achieved by this paper is the similarity searching by using Word2vec model which aims to better find information by applying an unstructured intelligent search that helps decision makers to get relevant intelligence on which to base their choices.
Outage Analysis of a D2D Network for MIMO-NOMA-based Downlink Transmission

This study investigates the performance of a relay-assisted cognitive radio (CR) enabled device-to-device (D2D) communication with Kernelized Energy Detection (KED). A D2D user uses KED technique for sensing the cellular user (CU) channel and uses the same while it is found to be idle. The D2D communication system uses multiple-input and multiple-output (MIMO), and non-orthogonal multiple access (NOMA) techniques to reduce the bad impact of fading and improve spectrum efficiency. The relay forwards the data received from a D2D source to a D2D destination and at each destination device, the received signals are combined using the maximal ratio combining (MRC) technique. The outage probability is studied as a performance metric. An analytical model of the outage probability for the considered network scenario is developed. A simulation framework has been developed and validated with the analytical framework.
Voice Controlled Home Automation System using Google Assistants

One of the most rapidly developing technologies that helps reduce the amount of work done by humans is known as “home automation. ” People who are looking for luxury and sophisticated home automation platforms are the target audience for some home automation systems. On the other hand, some systems are designed to be user-friendly for people who have special needs, such as the elderly and the disabled. Home automation and the use of applications that are controlled by voice are becoming more common as a result of advancements in technologies such as Google Assistance and Amazon Alexa. Now, we have developed our own version of this project, which is called “Voice Controlled Home Automation Using Google Assistant. ” The goal of this paper is to design a home automation board that is both functional and aesthetically pleasing, and that can be concealed within the AC power units that are mounted on our walls. We are able to control all of the gadgets that are linked to the board by giving it a simple voice command such as “Okay, Google, turn on the lights. ” This will cause the lights to turn on. Because of this paper, we are able to control our household appliances online, manually, and using a timer.
Exploring Possible Applications of ORB SLAM 2 in Education, Healthcare, and Industry: Insights into the Challenges, Features, and Effects

In this research we explore the ORB SLAM 2 which is a state-of-the-art algorithm for Simultaneous Localization and Mapping (SLAM) and which has been a cornerstone of SLAM Algorithm developments. In this paper, we highlight and provide insight into the possible applications of ORB SLAM 2 in the domains of Education, Industry and Healthcare. During the explanation of the working of ORB SLAM 2, we highlight the challenges, its features and its effects. We explore on how ORB-SLAM 2 can be used for a variety of purposes, including in Autonomous Car Driving. Robotics, mapping the surroundings, Augmented and Virtual Reality.
Elasticity Parameter Estimation in a Simple Measurement Setup

Elastic deformation has wide applications in medical simulations, therefore when it comes to designing physical behavior of objects for realistic training applications, determining material parameters so that objects behave in a desired way becomes a crucial. In this work we consider the problem of elasticity parameter estimation for deformable bodies, which is important for accurate medical simulations. Our work has two major steps: the first step is the data acquisition and preparation, and the second step is the parameter estimation. The experimental setup for data acquisition consists of depth and force sensors. Surface deformations are acquired as a series of images along with the corresponding applied forces. The contact point of the force sensor on the surface is found visually and the corresponding applied forces are acquired directly from the force sensor. A complete mesh deformation which is obtained from a surface tracking method is employed along with force measurements in the elasticity parameter estimation method. Our approach to estimate the physical material properties is based on an inverse linear finite element method. We have experimented with two approaches to optimize the elasticity parameters: a linear iterative method and a force-displacement error minimization method. The two methods were tested on the simulation data, and the second method was tested on three deformable objects. The results are presented for the data captured by two different depth sensors. The result is a set of two parameters, the Young’s modulus and the Poisson’s ratio, which represents the stiffness of the object under test.
A Novel Framework on QoS in IoT Applications for Improvising Adaptability and Distributiveness

The Internet of Things, or IoT, is a broad term referring to a pattern of technology that can detect, compute, transmit, and store the data that must be exchanged and stored in end user applications and systems. IoT also performs the functions of gathering the information, processing the information, analyzing the data. Due to wider range of scope and increase in number of IoT devices, it has become a critical challenge in meeting the demands of Quality of Service (QoS) in IoT devices. So there is a great need to access various parameters of QoS based on many perspectives, likely user specific, operator and network specific, depending on communication resources, edges nodes and all other enabling technologies.
Effectiveness of Game Based Learning in Problem Based Learning

Problem based learning has been adapted by various universities and inscribed with perspectives based on the demographic challenges. The method known to be effective for producing lifelong learners and improve cognitive skills, has been experimented on several facades. Problems, being major drivers in the process, have been researched to design and the deliberate the most effective ways. Through this paper we propose a game based learning model which can be used as problems in a problem based learning framework. In contrast to learning activities supplied using more old-fashioned didactic methodologies, this strategy can be used to engage a student through meaningful activities. This paper presents a model to use game in problem based learning followed by a case study and its analysis. Giglane game was used in the process to analyze the effectiveness with respect to number of stakeholders and incidents reported. The method is validated for effectiveness and promises to be an effective strategy for reflections and self-directed learning attributes. The method directly supports the decomposition structure from the computational thinking methodology which can be combined with the problem based learning process.
A Nonlinear Programming Solver based on Battery Efficiency Maximization for Quasi-Z-source Cascaded H- bridge Multilevel Inverter with PV and Battery

A nonlinear programming solver is used in this paper to optimize a proposed objective function to handle the energy management system (EMS) of a grid-connected quasi-Z-source cascaded H-bridge multilevel inverters with energy storage (ES-qZS-CHBMLIs) for photovoltaic (PV) plants. The solver (fmincon) optimizes a constrained nonlinear multivariable function based on maximizing the battery energy storage system (BES) efficiency. The proposed EMS is responsible for producing the demanded power while dispatching energy among the BES. Considering different battery parameters for each cascaded module working under variable grid references and PV powers, the efficiency is optimized for different conditions. Common control topologies are used for balancing the voltage level on the DC link, enhancing output power quality, reliability, and ensuring optimal operation of the PV power plants. A grid-connected single-phase configuration is designed in MATLAB-Simulink to evaluate the effectiveness of the proposed EMS. Every cascaded qZSI is connected to a 4.8 kW PV power plant, operating in different irradiance conditions, to validate the proposed configuration and the control system.
A 0.5 V Modified Pseudo-Differential Current-Starved Ring-VCO with Linearity Improvement for IoT Devices

Ring-VCO (Voltage controlled oscillator)-based ADCs are suitable for data acquisition in IoT devices due to simple delay elements configuration in the chain. The future development of IoT devices will be directed towards ultra-low operating voltage and reduce their size and area cost. However, the low operating voltage further degrades the linear voltage-to-frequency (V-to-F) tuning characteristics of ring-VCO. The ring-VCO based on complementary bias voltage controls approach achieved the linear V-to-F tuning characteristic at sub-/near threshold operating voltage. The ring-VCO relied on the current-starved pseudo-differential ring-VCO configuration in which a huge number of transistors occupied one delay element. This paper presents the novel current-starved pseudo-differential delay element, which has decreased overhead transistors by 37.5% and sizing by 44.88% compared with the previous work while not adversely affecting the linearity of V-to-F tuning characteristic. Simulation results with a 0.5V operating circuit designed in TSMC $0.18\mu\mathrm{m}$ CMOS technology shows that the maximum nonlinearity error is below 1.3% for 4-stage and phase noise is 95.01dBc/Hz at 1MHz offset frequency.
Combination of RFM’s (Recency Frequency Monetary) Method and Agglomerative Ward’s Method for Donors Segmentation

Customer segmentation is a technique to improve the company's relationship with its customers. The RFM method is often used when segmenting customers. The RFM (Recency Frequency and Monetary) method is used to determine the character of each customer based on the transaction data made. Each variable is then given a weight to determine the final score. Final scores are used to divide customers into several segments. This research will replace the scoring procedure with RFM-based cluster analysis. Ward's Method is a Hierarchical Agglomerative Clustering technique that has high memory and time complexity. The cluster technique has the advantage of maximizing the homogeneity of group members and minimizing variance within the group. Customers will be segmented using this clustering algorithm and RFM. The data used is 10070 data and is divided into 2 datasets. Data is partitioned to reduce memory usage and outlier handling. Outlier data has 991 data and normally distributed data has 9079 data. With a maximum evaluation result of 0.936 in the distribution of 2 clusters for outlier data and a maximum evaluation result of 0.345 in the distribution of 3 clusters for normal distribution data. It follows that outlier data has significant heterogeneity between groups, while data with a normal distribution has low heterogeneity.
Factor Affecting Intention to Use Digital Banking: A Research in Indonesia

The evolution of information technology, societal demands on an application’s operating system, and the increasing specifications and sophistication of smartphones necessitate banks modifying their mobile banking applications. The transformation of mobile banking to digital banking conducted by banks has prompted users to reevaluate their preferences. Digital banking is a contemporary type of banking – a new type of digital enterprise. It encompasses all facets of a conventional bank. Through digital technology, it is converted into an application. This study employed the statistical technique of Structural Equation Modeling (SEM) with SMART PLS as an instrument. Using Google Forms, data from 449 respondents were gathered online, but only 364 of the responses were valid. The respondents are digital banking customers from Jakarta, Bogor, Tangerang, Bekasi, Depok, and other cities in Indonesia. This research uses the Purposive Sampling method; data was collected in November 2022. One of the eleven hypotheses regarding the influence of digital banking on user intent is insignificant. This research’s implication contributes to determining the most effective communication and strategy so that more users with greater benefits can utilize digital banking.
PCSVM: A Hybrid Approach using Particle Swarm Optimization and Cuckoo Search for Effective Cancer Diagnosis

Today, cancer is increasingly recognized as a significant contributor to the rising death toll around the globe. Therefore, early identification of cancer raises the degree to which patients may recover, and one of the main concepts of this approach is known as machine learning (ML). For ML the important factor is the presence of a proper dataset and biopsy and microarray datasets are two varieties of datasets available to help in developing ML-based models. The biopsy dataset does not have any genetic information and due to this limitation present behind the biopsy dataset, the researchers are looking at the microarray data. The microarray data are used for the diagnosis and classification of cancer disease which makes the data colossal. But to examine a large number of datasets is a challenging task. To avoid this kind of issue feature selection is one of the best solution and classification algorithms are present in machine learning which selects the relevant features that help in constructing a better model for classification. As a result of this better accuracy is obtained in disease classification which helps in prevention. The primary objective of this research work is to provide an integrated model PCSVM or PSO-CS-SVM based on Particle swarm optimization (PSO), and cuckoo search algorithm (CS) algorithm used as feature selection and optimization algorithm respectively. In addition, the support vector machine (SVM) is being used as the classifier. The state-of-the-art approaches to machine learning, such as Decision Tree, Neural Network, and Random Forest, are used to compare the performance of the proposed PSO-CS-SVM.