Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extensive Review and Comparison of Different Machine Learning Algorithms for Customer Behaviour Pattern Analysis

Consumer shopping behavior is changing due to the explosive growth of e-commerce, which brings both benefits and challenges. E-commerce enterprises must comprehend the shopping behavior of their clients to enhance user experiences and increase consumer satisfaction. This emphasizes how crucial it is to research consumer behavior in the context of online shopping. Customer transactions on e-commerce platforms produce enormous data from clicking, buying histories, demographics, and beyond. Integrating the findings from investigations offers a comprehensive understanding of buyers. In this paper, we have thoroughly analyzed and used various machine learning algorithms (Apriori algorithm, Ensemble learning, KNN, Naive Bayes, NLP) to analyze customer behavior. We have also evaluated the performance of various algorithms in terms of accuracy and time taken to analyze the data of consumers' shopping behavior.
Forecasting Stack Flue Gas Exit Temperature in Electrostatic Precipitators Using Hybrid Deep Learning Model

Forecasting of Time Series Data has been studied over a long period of time and several techniques based on statistical methods and machine learning have been suggested to solve the problem. However, success of most of those methods depends on carefully curating the input features (predictors) which have the maximum influence on the predicted variable. In the event, the input features are not fully defined, or not all-important features are captured, majority of these models fail to predict a meaningful value. In this paper, a Self-Exploratory Hybrid Deep Learning Model is presented which introduces the paradigm that when all the exogeneous variables impacting the target variable are unavailable to the model, it is still possible to predict a meaningful value by observing the covariates of the target variable that are indirectly associated or weakly associated with the target but having causal impact on the target variable. The model presented in this paper is Hybrid in nature consisting of a Cognitive Orchestration Unit and a set of Worker models based on deep learning. The Cognitive Orchestrator is the brain of the hybrid model and operates in a self-exploratory mode following the principles of Learn, Explore, Remember, Evolve and Reward. Based on the rules provided, the Cognitive Orchestrator module goes through a set of learning, exploring and evolving phases using different algorithms which are based on Symbolic Regression, Temporal Convolution Network with Attention Block, 1DConvolution Block with LSTM and Deep Feedforward Neural Network. The Cognitive Orchestration Unit modulates the hyperparameters of the individual models to arrive at the optimum value based on which the stack (exit flue gas) temperature coming out of electrostatic precipitator is predicted.
Analyzing Credit Card Defaulters: A Comparative Study Using Kohonen Maps, Neural Networks, and Decision Trees

The text discusses the credit card debt crisis faced by credit card issuers in Taiwan, which has led to a decrease in consumer financial confidence and poses problems for both banks and cardholders. There is a study conducted using data on customers who took credit cards in order to identify the most important criteria for non-payers. The study aims to predict loan repayment ability using a neural network and selecting the most significant factors. The literature review highlights the use of neural networks in determining non-payers and the potential for creating systems to warn credit card owners about risk behavior. The methodology involves data processing using the Kohonen map, a type of neural network that uses unsupervised learning for visualization and clustering. Decision Trees and Neural Networks are also used in the analysis. Analysis using Kohonen maps did not provide the necessary information due to heterogeneous data in clusters. Analysis via a neural network resulted in a high error percentage of 17.6% and lacked accuracy in predicting average values. Analysis through the decision tree yielded a lower error percentage of 12.5% and identified repayment status in September and August as the most significant factors. An experiment was conducted by dividing factors into logical groups, resulting in a difference of 7% in error percentages for each group. The conclusion suggests that the decision tree method is the most suitable for this data model, with repayment status in September as the key factor in predicting defaulters.
Efficient Adaptive Federated Learning in Resource-Constrained IoT Environments

Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework which enables IoT devices to collaboratively train machine learning models vi-a sharing model parameters. However, inefficiency due to frequent parameters transmissions significantly reduces FL performance. Existing acceleration algorithms for speeding up FL training consist of two main types including local update and parameter compression which consider the trade-offs between communication and computation/precision respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, we propose an efficient adaptive federated optimization (EAFO) algorithm to improve the efficiency of FL in resource-constrained IoT environments, which minimizes the learning error by the joint consideration of two variables consisting of the local update and parameter compression. The EAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate the high effective-ness of the proposed EAFO algorithm, which can achieve higher accuracies faster compared with the state-of-the-art algorithms.
Research on machine vision decision-making system for self-driving logistics vehicles

Aiming at the integrated problem of perception and decision-making of self-driving logistics vehicles in complex traffic environments, this study starts from four aspects: environment perception and cognition, scene understanding and analysis, intelligent decision-making and obstacle avoidance, and optimized path planning. Deep convolutional neural network models are utilized to extract, analyze and understand sensory data, and multi-sensor data fusion technology is used to integrate the information. Reinforcement learning methods are applied for the system to automatically learn decision-making strategies, while path planning algorithms are used to find the optimal driving path. In the simulation experiments of the machine vision decision-making system using Gazebo, the recognition accuracy reaches 98.2%. The results demonstrate the feasibility of the technical solution for an automated driving machine vision decision-making system.
Identification of Molecular Biomarkers and Key Pathways among Idiopathic pulmonary fibrosis (IPF), Chronic Obstructive Pulmonary Disease (COPD) and Lung cancer

Lung cancer (LC), idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the most fatal disorders in the globe, generating frequent human issues. Having IPF and COPD are the risk factors of the LC, but the molecular mechanisms that underlie among IPF, COPD, and LC are not yet elucidated. In this study, we looked for shared molecular indicators and pathways that might explain how individuals with IPF, COPD, and LC are related to one another. GSE24206, GSE76925 and GSE18842 microarray datasets are utilized for IPF, COPD and LC samples. The preprocessing of datasets has done using R language, and then concordant differentially expressed genes (DEGs) are discovered. After that the protein-protein interactions (PPIs) are built using the similar DEGs, and the hub genes are determined using topological analysis. ETS1, MSH2, SORD, RORA and NEDD9 are the PPI network’s top 5 hub genes. The pathways of KEGG demonstrated that the concordant DEGs are related to the colorectal cancer and pathways in cancer. Future work for this project will focus on miRNA, TF, and gene ontology (GO) analyses, as well as module analysis networks. Finally, based on the concordant DEGs, a number of potential medications have been suggested.
Digital Twin Assisted Closed-Loops for Energy-Efficient Open RAN-Based Fixed Wireless Access Provisioning in Rural Areas

For digital inclusion, Internet quality in Low-Density and Rural Areas (LDRAs) should be enhanced to satisfy QoS requirements of various services and applications. Due to the high operating costs of fiber optic deployment in LDRAs, 5G Fixed Wireless Access (5G FWA) is becoming a more attractive solution. Furthermore, 5G services require edge cloud deployment for proximity computation, which increases both required network and energy resources. Therefore, we propose closed-loops assisted by Digital Twin (DT) for energy-efficient Open RAN-based FWA provisioning in LDRAs. We consider a 5G FWA and edge cloud system as Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances that manage network slices for scheduling purposes. We propose another closed-loop for intra-slice resource allocation to LDRAs. We develop an energy model and join radio resource allocation with the energy model. Then, we design reinforcement learning and optimization approaches to maximize delay requirement satisfaction while minimizing energy cost. Finally, we present DT replicating PT by incorporating solution experiences into future states. The results show that our approach uses energy resources efficiently while satisfying delay requirements of slices.
A Tensor Algebra Compiler for Sparse Differentiation

Sparse tensors are prevalent in many data-intensive applications. However, existing automatic differentiation (AD) frameworks are tailored towards dense tensors, which makes it a challenge to efficiently compute gradients through sparse tensor operations. This is due to irregular sparsity patterns that can result in substantial memory and computational overheads. We propose a novel framework that enables the efficient AD of sparse tensors. The key aspects of our work include a compilation pipeline leveraging two intermediate DSLs with AD-agnostic domain-specific optimizations followed by efficient C++ code generation. We showcase the effectiveness of our framework in terms of performance and scalability through extensive experimentation, outperforming state-of-the-art alternatives across a variety of synthetic and real-world datasets.
Interference-Free MU-MISO Symbiotic Radios via RIS Partitioning Design

This paper explores the potential of reconfigurable intelligent surface (RIS) to null interference in a multi-user multiple-input single-output (MU-MISO) symbiotic radio (SR) system. Due to the periodic variations of the RIS phase shifts in the SR systems, it is challenging to use it to achieve interference-free reception in SR systems. To address this issue, we propose a RIS partitioning design for MU-MISO SR. By splitting RIS into two parts, one serves as a relay and removes interference among users, and the other one acts as a secondary transmitter for the Internet of Things (IoT) system, transmitting IoT information by periodically changing its phase shifts. Through this design, the MU-MISO SR system can be decomposed into multiple parallel MISO SR subsystems, each of which only contains the desired user signal and the RIS signal without interference from other users. To do so, we first explore the relationship between the number of reflecting elements and their interference-free capability. Then we formulate a power minimization problem to jointly optimize the transmit beamforming and the phase shifts of partitioned RIS. Finally, simulation results show that the proposed RIS partitioning design could help reduce power consumption, while at the same time achieving interference-free reception in SR.
A Novel Lightweight Group Authentication Protocol for Internet of Things

The rapid proliferation of embedded devices has driven the expansion of the Internet of Things (IoT) across various sectors. However, this growth has exposed a vulnerability, especially in smart homes, where attackers attempt to exploit security weaknesses. Furthermore, many existing user authentication methods in the literature come with added computational complexity and require enhancements to withstand targeted attacks on smart homes. In this work, we develop a novel lightweight authentication protocol designed to enhance the security of IoT applications. The lightweight nature of our solution is suitable for deployment on heterogeneous and resource-constrained IoT devices. Besides, we leverage Shamir's Secret Sharing to establish mutual authentication among the participating entities. We validate the security of the proposed protocol using the Scyther protocol verification tool and prototype it on a Raspberry Pi to analyze the power consumption. Our study reveals that the proposed protocol outperforms existing schemes by consuming 0.52 seconds on average, 23.88% of CPU resources, and 367 MB of memory.
A Comprehensive Study of Scene Text Recognition in Scene Text Image Super-Resolution with Parametric Frameworks

Scene Text Recognition (STR) is a technique to detect and recognize text in images. Predicting text in real-world scene images is challenging due to various uncontrollable environmental factors. State-of-the-art text detection and recognition models leverage deep learning and Transformer architectures, consequently achieving impressive accuracy on benchmark datasets. However, challenges persist in accurately processing text within real-world images, often due to unseen data or limited datasets. Both the limitations of STR and the quality of scene text images are crucial factors. Recently, a parametric weight and multiple parametric regularizations were proposed to improve the quality of real-world scene text images. Different from previous surveys in this area, this study has three main objectives. Firstly, to confirm the performance of parametric methods, the text recognition accuracy between with and without methods is compared by using different STR methods. Second, to make the comprehensive experiments, the outcomes of each STR method are compared to show their prediction performances. Third, several existing challenges and research directions are discussed.
Procedure-Aware Stateless Systems for 5G & Beyond Core Networks

As public and private cloud-native deployments of the 5G Core (5GC) networks are rolling-out on a wide scale, attention is shifting towards efficient state management. While stateful deployments were the default method in the previous generations of mobile networks, they lack the necessary flexibility that cloud-native orchestration demands. Yet, traditional approaches taken to enable stateless deployments require the operators to sacrifice on performance due to the frequent state transactions. To overcome this issue, in this paper we propose a Piggyback-based and a Proactive-Push approach which allow for procedure-aware stateless 5GC systems. Our evaluations highlight the advantages of the Piggyback approach for two synchronous control procedures, reducing their completion time by ~44% and ~70% compared to the baseline. For asynchronous procedures, the Proactive-Push approach outperforms the baseline with ~13% and ~22%. More importantly, these mechanisms do not pose additional overhead on CPU and bandwidth utilization.
Big Data and Artificial Intelligence: Revolutionizing Business Decision-Making

The term artificial intelligence (AI) refers to the fourth industrial revolution. Globally, artificial intelligence and big data have revolutionized all sectors. Artificial intelligence has grown into a revolutionary technology with profound implications on many industries, included business, thanks to the usage of big data. Artificial intelligence (AI) and big data has transformed decision-making procedures in the past few years by giving businesses access to sophisticated analytical tools that let them glean insightful information from massive volumes of data. Artificial intelligence segments machine learning and deep learning have been extensively utilized to tackle and enhance a wide range of business issues, including marketing, credit card fraud detection, algorithmic trading, customer support, demonstrating products according to consumer preferences, and insurance underwriting. Using AI and big data into marketing techniques may help a business owner boost audience response and create a powerful online brand that can compete with competitors. Examining how big data and decision-making are used in artificial intelligence applications in business settings is the main objective of the research. In specifically, this research looks at how artificial intelligence is being used to improve decision-making processes and how that's affecting corporate structures. According to the report, artificial intelligence plays a revolutionary role in business decision-making, providing a host of benefits related to effectiveness, precision, and creativity.
Toward Realistic Human Crowd Simulations with Data-Driven Parameter Space Exploration

Understanding human crowd motion is crucial for realistic crowd simulations and content creation. Over the multiple decades, several computational algorithms have been developed to devise a more realistic simulation that will vastly improve the user experiences. However, the gap between real and simulated human motion and behavior is still large. One of the promising thrusts of the crowd simulation community to reduce the gap is to utilize a data-driven trajectory prediction model, using the access to a large amount of data. However, building a crowd simulation model based on the learned microscopic trajectory model is still a challenging task. In addition, unlike individual or group-level human trajectory data, large-scale real human crowd motion data is not readily available. To overcome these, we investigate the utility of synthetic simulation data. We propose a novel human crowd motion estimation framework that can predict simulator parameters from trajectory data. Our initial findings show promising results that our method can robustly estimate simulation parameters.
Shelf-Life Prediction of Oil Palm Fresh Fruit Bunches Using Artificial Neural Networks

Indonesia, one of the world’s main producers of oil palm fresh fruit, boasts a Crude Palm Oil (CPO) export performance index of 0.94. Predicting the shelf life before processing oil palm fresh fruit is critical. However, the challenge lies in accurately estimating when ripe fruit will spoil, leading to potential rot during storage or distribution. This research introduces an innovative approach for accurately predicting the shelf life of oil palm fruit by utilizing a Multilayer Perceptron-type artificial neural network. The shelf-life prediction is calculated based on measurements from electrical resistivity, gas, and color sensors on oil palm fresh fruit processed using an Arduino Nano. The application of artificial neural networks in predicting the shelf life of oil palm fruit achieves an accuracy level of 98.62%, with a Mean Square Error (MSE) value of 0.0069. Consequently, the system can predict when ripe oil palm fruit will rot.
A Hybrid Approach Using SVM, kNN and Random Forest for ECG Classification

A concerned health problem that has to be taken care of which is causing illness and death is cardiac arrhythmia. Consequently, the use of effective tools to predict and diagnose this condition is highly desirable. Arrhythmia prediction is the main focus of this research, which examines three main content areas: machine learning model selection, electrocardiogram (ECG) feature selection, and model performance evaluation. The goal is to formulate machine learning boundaries that can accurately use ECG signals to predict simple heart arrhythmias. The potential of this study to improve clinical practice and patient outcomes and to guide future research in this area is of great importance and is essential for the development of accurate tools for the diagnosis of cardiomyopathy using ECG features a most effective identification and predictive machine learning models, and can help formulate treatment options.
Real-Time Human Activity Recognition Using Dimensionality Reduction in Wireless Multimedia Sensor Networks

Human Activity Recognition (HAR) has emerged as a crucial assistive technology in elderly healthcare, providing caregivers with the ability to monitor and assist in daily activities. HAR is typically conducted through the analysis of data obtained from various types of sensors, including body, object, and ambient sensors. This study focused specifically on utilizing data from internal sensors to recognize activities such as walking, ascending/descending stairs, sitting, standing, and falling. Data was gathered from smartphones, and all HAR models were tested using real-time data collected from Wireless Multimedia Sensor Networks (WMSNs). To perform activity recognition, neural network algorithms such as CNN and LSTM, along with traditional machine learning classification algorithms such as SVM, KNN, and Random Forest Classifier were employed. Additionally, dimension reduction techniques are utilized to decrease the number of features, thus reducing computational time and energy consumption. Furthermore, transfer learning was employed with different scenarios to improve accuracy. Finally, all functions were implemented and compared in two different WMSN architectures. The results enabled us to identify the most efficient and accurate methods for HAR utilizing data from internal sensors. This research has the potential to enhance the quality of life for the elderly and provide caregivers with the necessary tools to provide better care.
Ensemble Machine Learning for Multi-Class Classification Tasks

Ensemble learning is the process of amalgamating multiple classifiers to engender a strong classifier in supervised machine learning. It boost-up the performance week learning algorithms. In this paper, we have tested the performance of RandonForest, Bagging, and Boosting methods applying decision tree, Naïve Bayes classifier, and NBTree as a base classifier on 10 benchmark datasets. RandomForest and bagging methods use a majority voting technique, whereas boosting (AdaBoost) algorithm uses a weighted majority technique to classify new instances. A top-down recursive divide and conquer recursive algorithm is decision tree as well as naïve Bayes classifier work as a deterministic classifier. NBTree is a combination of a decision tree and naïve Bayes classifier where nodes of a decision tree are replaced by Bayes models. The experimental results demonstrate how flexible and powerful ensemble learning approaches are for dealing with classification problems. In order to help practitioners choose the best ensemble approach, several methods are methodically compared to show their respective strengths and disadvantages.
Transforming Healthcare Records: IoT-Based Wearable Medical Assessment System

IoT-based medical wearable device developed for continuous health monitoring, with a focus on establishing Electronic Health Records (EHRs). The device employs Internet of Things (IoT) technology, allowing the patient to passively communicate crucial live information such as blood pressure, oxygen saturation, pulse, and temperature. Data is wirelessly transmitted to a remote server for analysis and monitoring. In Bangladesh, where access to healthcare data can be limited, this unique wearable device has enormous potential as a proof of concept in bridging the gap and assuring continuous health monitoring. Individuals can use the device to remotely monitor their vital signs and receive medical treatment when needed. The major findings of this project emphasize the accuracy and dependability of the gadget in assessing vital signs and extending accessibility to a mass market through statistical analysis of data acquired from Bangladeshi consumers.