Incorporating Sustainability: A Comprehensive Review of Factors Influencing Consumer Acceptance of Mobile Wallets

Mobile wallets have gained widespread popularity as a convenient, secure, and user-friendly payment method embraced by consumers. However, the pace of mobile wallet adoption has exhibited variations across different markets, primarily due to a range of factors. In this study, we present an all-encompassing examination of existing literature, aiming to pinpoint the fundamental elements influencing the sustainable acceptance of mobile wallets by consumers. Through an exhaustive analysis of 80 research papers published between 2010 and 2022, we discern the prevailing factors that hold sway over the adoption of mobile wallets. Our scrutinization highlights that factor such as perceived sustainability, usefulness, ease of use, security, social influence, trustworthiness, and compatibility stand out as the most formidable propellants of mobile wallet adoption. Furthermore, our investigation uncovers hurdles that hinder the wider acceptance of mobile wallets, encompassing insufficient awareness, perceived intricacies, and lingering uncertainties regarding the technology’s sustainability. Our in-depth evaluation underscores the necessity of comprehending consumer perspectives and dispositions towards mobile wallets to galvanize their adoption sustainably. The culmination of our inquiry involves a discourse on the implications drawn from our discoveries, catering to researchers and practitioners vested in fostering the sustainable adoption of mobile wallets

Techniques of Machine Learning for the Purpose of Predicting Diabetes Risk in PIMA Indians

Chronic Metabolic Syndrome Diabetes is often called a “silent killer” due to how little symptoms appear early on. High blood sugar occurs in people with diabetes because their bodies have a hard time maintaining normal glucose levels. Care for a recurrent sickness would be permanent. The two most common forms of diabetes are type 1 and type 2. A better prognosis can help reduce the high risk of developing diabetes. In order to better predict the likelihood that a PIMA Indian may develop diabetes, this study will use a machine learning-based algorithm. The demographic and health records of 768 PIMA Indians were used in the analysis. Standardisation, feature selection, missing value filling, and outlier rejection were all parts of the data preparation process. Machine learning techniques such as logistic regression, decision trees, random forests, the KNN model, the AdaBoost classifier, the Naive Bayes model, and the XGBoost model were used in the study. Accuracy, precision, recall, and F1 score were the only metrics utilised to assess the models' efficacy. The results demonstrate that. The results of this study reveal that diabetes risk may be reliably predicted using machine learning-based models, which has important implications for the early detection and prevention of this illness among PIMA Indians

A model for multi-attack classification to improve intrusion detection performance using deep learning approaches

This proposed model introduces novel deep learning methodologies. The objective here is to create a reliable intrusion detection mechanism to help identify malicious attacks. Deep learning based solution framework is developed consisting of three approaches. The first approach is Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) with seven optimizer functions such as adamax, SGD, adagrad, adam, RMSprop, nadam and adadelta. The model is evaluated on NSL-KDD dataset and classified multi attack classification. The model has outperformed with adamax optimizer in terms of accuracy, detection rate and low false alarm rate. The results of LSTM-RNN with adamax optimizer is compared with existing shallow machine and deep learning models in terms of accuracy, detection rate and low false alarm rate. The multi model methodology consisting of Recurrent Neural Network (RNN), Long-Short Term Memory Recurrent Neural Network (LSTM-RNN), and Deep Neural Network (DNN). The multi models are evaluated on bench mark datasets such as KDD’99, NSL-KDD, and UNSWNB15 datasets. The models self-learnt the features and classifies the attack classes as multi-attack classification. The models RNN, and LSTM-RNN provide considerable performance compared to other existing methods on KDD’99 and NSL-KDD dataset

Network Intrusion Detection using ML Techniques for Sustainable Information System

Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explores various machine learning algorithms employed for intrusion detection and evaluates their performance. Findings indicate that machine learning algorithms exhibit a significant improvement in intrusion detection accuracy compared to traditional methods, achieving an accuracy rate of approximately 90 percent. It is worth noting that the previous work experienced computational challenges due to the time-consuming nature of the utilized algorithm when processing datasets. In this paper, we propose the exertion of more efficient algorithms to compute datasets, resulting in reduced processing time and increased precision compared to other algorithms to provide sustainability. This approach proves particularly when computational resources are limited or when the relationship between features and target variables is relatively straightforward

IoT Network Attack Severity Classification

Lack of network security is a major roadblock for Internet of Things (IoT) implementations. New attacks have emerged in recent times, taking advantage of vulnerabilities in IoT gadgets. The sheer scale of the IoT will also make standard network attacks more potent. Machine learning has found a lot of use in traffic classification and intrusion detection. We present a methodology in this piece that can be used to spot fraudulent communications and determine the identity of IoT devices. To determine the origin of the generated traffic, the nature of the traffic, and the presence of network hazards, this framework collects features per network flow. To achieve this, it relocates the network’s brains to its periphery. In order to discover which of several Machine Learning algorithms is superior to random forest, a number of them are pitted against one another. Using these Machine Learning methods, attacks can be ranked in terms of their potential damage. After running the tests, it was determined that TABNET has the highest accuracy (94.62%) for categorizing the network severity (93.51%) and that CNN has the lowest accuracy (93.51%) of the two

Automated Brain Tumour Classification using Deep Learning Technique

Brain Tumour is a severe condition caused due to abnormal growth of cells in the brain. Brain Tumour is broadly classified into two categories namely Malignant (Cancerous) and Benign (Non-Cancerous). As tumour grows, the pressure within the skull can increase which can damage the brain and be life-threatening. Early detection and classification of the brain tumours is important as it helps to select the most appropriate treatment for saving the patient’s life. Usually, Brain Tumour Detection can be done manually by the doctors or use machine learning models in case of MRI images of the brain. In literature, it is identified that deep learning techniques such as CNN, DCNN and RNN show good results in image processing applications. This paper aims to detect and classify the Brain Tumours effectively using CNN deep learning techniques. The dataset is collected from Kaggle. The proposed method achieved an accuracy of 93.5% and 98.4% with CNN and Resnet50 respectively

Modelling the Impact of Road Dust on Air Pollution: A Sustainable System Dynamics Approach

Road dust contributes significantly to air pollution by releasing fine particulate matter (PM) and other pollutants into the air, which can cause respiratory and cardiovascular problems and premature death. This dust is generated through the wear and tear of vehicle tires and road surfaces, as well as the accumulation of dirt and debris on the road, primarily from construction activities and cargo trucks carrying building materials. Wind, weather conditions, and vehicle movement play crucial roles in the distribution and concentration of these particles in the air. To address this issue, this paper focuses on identifying various variables that are connected to road dust operations and their interrelationships with air pollution variables, representing the dynamic pattern of the entire system. The paper proposes the establishment of a sustainable causal-loop model using system dynamics (SD) modeling in Vensim, connecting feedback mechanisms to effectively control the road dust concentration. Additionally, the paper suggests different policy interventions applied to the whole system to achieve optimized results. In the future, this research aims to convert and simulate the causal-loop model to a stock-flow model and compare the effectiveness of different policy interventions to further reduce road dust contributing to air pollution

Moving object detection using modified GMM based background subtraction

Academics have become increasingly interested in creating cutting-edge technologies to enhance Intelligent Video Surveillance (IVS) performance in terms of accuracy, speed, complexity, and deployment. It has been noted that precise object detection is the only way for IVS to function well in higher level applications including event interpretation, tracking, classification, and activity recognition. Through the use of cutting-edge techniques, the current study seeks to improve the performance accuracy of object detection techniques based on Gaussian Mixture Models (GMM). It is achieved by developing crucial phases in the object detecting process. In this study, it is discussed how to model each pixel as a mixture of Gaussians and how to update the model using an online k-means approximation. The adaptive mixture model's Gaussian distributions are then analyzed to identify which ones are more likely to be the product of a background process. Each pixel is categorized according to whether the background model is thought to include the Gaussian distribution that best depicts it