International Journal of Information Technology最新文献

Abstract-In the realm of distributed computing for large-scale data processing, MapReduce stands out for its efficiency. However, as tasks become increasingly compute-intensive, it faces challenges in single-node performance. In the context of breast cancer detection, particularly with image data, a new approach has emerged to enhance MapReduce through GPU acceleration. This implementation, executed using Hadoop and OpenCL, targets a general and cost-effective hardware platform, seamlessly integrating into Apache Hadoop. Tailored for a heterogeneous multi-machine and multicore architecture, this solution addresses the compute-intensive nature of big data applications in breast cancer image analysis. Remarkably, the implementation has achieved a significant nearly 13-fold improvement in performance, without the need for additional optimizations.

Humanity’s progress in combating hunger, poverty, and child mortality is marred by escalating environmental degradation due to rising greenhouse gas emissions and climate change impacts. Despite positive developments, ecosystems are suffering globally. Regional strategies for mitigating and adapting to climate change must be viewed from a global perspective. The 2015 UN Sustainable Development Goals reflect the challenge of balancing social and environmental aspects for sustainable development. Agriculture, vital for food production, also threatens Earth systems. A study examines the interplay of land-use impacts, modeling crop and livestock trade, and their effects on climate, biodiversity, water, and land using a Partial-Equilibrium Agricultural Sector Model. Different scenarios involving taxing externalities related to Earth processes were tested. Results show synergies in reducing emissions, biodiversity loss, water use, and phosphorus pollution, driven by shifts in crop management. Nitrogen application and deforestation scenarios exhibit weaker synergies and more conflicts. The study offers insights into SDG interactions and the potential for sustainable farming.

The escalating production of counterfeit notes, facilitated by advancements in color printing and scanning, poses a significant global challenge impacting economies and security. This issue, prevalent in countries like India, has negative ramifications, including the funding of illegal activities and terrorism. Despite efforts, such as demonetization in 2016, counterfeits persist, necessitating innovative solutions. The proposed model introduces a fake note detection system utilizing computer vision and machine learning, specifically a Convolutional Neural Network (CNN). CNN effectively extracts intricate features from input data, showcasing its proficiency in pattern recognition. Notably, the system focuses on individual security features within banknotes, distinguishing it from other approaches that analyze entire note images. The primary goal is swift and accurate detection and reduction of counterfeit circulation, contributing to the overall security of the economy. The proposed model resulted in an impressive accuracy of 91.66% for all the six security features in the Indian denomination of Rs. 500, 95.25% for all the six security features in the Indian denomination of Rs. 200, 92.66% for all the six security features in the Indian denomination of Rs.100.

This paper introduces a new methodology for optimization problems, combining the Grey Wolf Optimizer (GWO) with Simi-stochastic search processes. Intelligent optimizations represent an advanced approach in machine learning and computer applications, aiming to reduce the number of features used in the classification process. Optimizing bioinformatics datasets is crucial for information systems that classify data for intelligent tasks. The proposed A-Proactive Grey Wolf Optimization (A-GWO) solves stagnation in GWO by applying a dual search with a Simi-stochastic search. This target is achieved by distributing the population into two groups using a different search technique. The model's performance is evaluated using two benchmarks: the Evolutionary Computation Benchmark (CEC 2005) and seven popular biological datasets. A-GWO demonstrates highly improved efficiency in comparision to the original GWO and Particle Swarm Optimization (PSO). Specifically, it enhances exploration in 66% of CEC functions and achieves high accuracy in 70% of biological datasets.

Modern healthcare facilities are equipped with major difficulties, particularly in poor nations where there are insufficient high-quality hospitals and medical professionals in remote places. Healthcare has profited from artificial intelligence’s revolution in many other areas of life. A few issues with the current architecture of the store-and-forward method of conventional telemedicine are that it requires a local health center with a dedicated staff, medical equipment to prepare patient reports, and long turnaround time for receiving a diagnosis and medication details from a medical expert in a main hospital, the cost of local health centers, and the requirement for a Wi-Fi connection. In this work, we present a new intelligent healthcare system built on cutting-edge technology such as deep learning and the Internet of Things (IoT). This system has the intelligence to use a medical decision support system to sense and process patient data by making use of adaptive neuro fuzzy inference system (ANFIS). For those who live in rural places, this system offers an affordable solution. By contacting local hospitals, users can determine whether they have a serious health concern and seek appropriate treatment. Additionally, the experiment findings demonstrate that the suggested system is capable of providing health services due to its efficiency and intelligence.

Electrocardiogram (ECG) signal classification is a cornerstone of automated heart abnormality detection. Unlike the limitations of human interpretation, AI techniques can effectively identify subtle patterns in ECG signals. This makes ECG a powerful non-invasive tool for assessing cardiovascular health. Existing methods for classifying ECG signals while valuable, they still struggle to achieve both high sensitivity and specificity. This limitation hinders their ability to deliver accurate and timely diagnoses for cardiac conditions. These shortcomings emphasize the need for more effective techniques to improve the precision of ECG signal classification. In response to these challenges, this study introduces a novel approach, using an ensemble methodology, a machine learning technique to enhance the precision of ECG classification through the fusion of signal and wave features. The proposed methodology addresses two key challenges: the transformation of paper ECG recordings into one-dimensional digital signals amenable to machine learning algorithms and the automated extraction of diagnostically significant features including the P wave, QRS complex, and T wave. Validation of the proposed methodology encompasses a comprehensive evaluation on a heterogeneous dataset comprising real-world and publicly available online resources. Noteworthy aspects of the evaluation include considerations of both intra-patient variations and inter-patient discrepancies, thus reflecting real-world complexities. Notably, in the realm of machine learning, the study employs ensemble algorithms and a soft voting classifier to enhance classification accuracy and robustness. This paper contributes to the advancement of automated ECG classification, offering a promising avenue for precise and reliable cardiovascular health assessment.

Local clinics are pivotal in delivering primary healthcare, especially in economically disadvantaged areas like Vietnam’s Northwest. However, these regions face notable deficits in healthcare infrastructure. Digital transformation offers a promising solution. This study assesses the digital transformation readiness of 75 local clinics in Northwest Vietnam and investigates the impact of influential factors on this readiness. The study design involved collecting responses from clinic heads or designated representatives through a web-based survey. The sample size comprised 75 local clinics in Northwest Vietnam. Multiple linear regressions were utilised to examine the impact of influential factors on the clinics’ digital transformation readiness. Findings indicate a significant readiness gap among the surveyed clinics, with observed scores falling below the maximum achievable score of 290. Most clinics scored between 63.5 and 116, highlighting substantial room for improvement in digital preparedness. The study unveiled significant relationships between digital readiness and clinic attributes. Negative correlations included clinic head age and reliance on e-wallets. Positive associations included seniority, social media engagement, and clinic characteristics like education and technology use. The regression results highlight positive associations with clinic head seniority, clinic social accounts, personnel using smart devices, and online patient record integration. Conversely, negative associations were noted with clinic head age and e-wallet usage. The findings stress targeted support for older clinic leaders in digital adaptation, highlight experienced leadership’s role, note distractions from financial technologies, emphasise social media’s digital readiness impact, and stress technological adoption’s importance, plus digital record-keeping benefits for clinics and patient care.

Graph Neural Networks (GNNs) have attracted considerable attention due to their ability to analyze structured data represented as graphs. In invoice information extraction, GNNs have proven to be a powerful tool for automatically extracting relevant information from invoices, streamlining data entry processes, and improving efficiency. By modeling the invoice layout as a graph and exploiting the inherent structural dependencies, GNNs enable end-to-end extraction by encoding the graph structure and using deep learning techniques. This work proposes a Graph Convolution Network to extract information from invoices. Furthermore, an evaluation of the effect of filter sizes on the model’s accuracy was performed. We built an extraction model based on the filter size selected by the evaluation. We achieved the accuracy of the test set of 96.4% and the training set of 98.5% on the dataset of about 1.500 invoice images we collected.

There is a high-risk privacy issue related to the Internet of Medical Things (IoMT) because of a lack of security in critical and sensitive information. We propose a framework for securing medical data in Healthcare 5.0 generated by the IoMT. Our framework uses Hyperledger Fabric (HF), a permissioned blockchain platform, to provide a decentralized and tamper-proof system for data management and sharing. The proposed framework includes modules for identity management, data encryption, and access control for Healthcare 5.0. Hyperledger is a Linux Foundation-hosted open-source project. The HF is one of the IBM-developed initiatives that eventually contributed to Hyperledger. The paper presents the architecture of the framework, as well as a prototype implementation and evaluation of its performance and security specific to consumer electronics for Smart Cities. This evaluation demonstrated that the proposed framework is efficient and effective at securing medical data in the IoMT, and could be used to develop secure and scalable healthcare systems in the future.

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.