International Journal of Informatics and Communication Technology (IJ-ICT)
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Alzheimer’s disease diagnosis using convolutional neural networks model
Get PDFThe global healthcare system and related fields are experiencing extensive transformations, taking inspiration from past trends to plan for a technologically advanced society. Neurodegenerative diseases are among the illnesses that are hardest to treat. Alzheimer’s disease is one of these conditions and is one of the leading causes of dementia. Due to the lack of permanent treatment and the complexity of managing symptoms as the severity grows, it is crucial to catch Alzheimer’s disease early. The objective of this study was to develop a convolutional neural network (CNN)-based model to diagnose early-stage Alzheimer’s disease more accurately and with less data loss than methods previously discovered. CNN, is adept at processing and recognising images and has been employed in various diagnostic tools and research in the healthcare sector, showing limitless potential. Convolutional, pooling and fully linked layers are the common layers that make up a CNN. In this paper, five CNN modelswere randomly chosen (ResNet, DenseNet, MobileNet, Inception, and Xception) and were trained. ResNet performed the best and was chosen to undergo additional modifications to improve accuracy to 95.5%. This was a remarkable achievement that made us hopeful for the performance of this model in larger datasets as well as other disease detection
Optimized support vector machine for sentiment analysis of game reviews
Get PDFThe rapid development of games has made game categories diverse, so there are many opinions about games that have been released. Sentiment analysis on game reviews is needed to attract potential players. Sentiment analysis is carried out using the support vector machine (SVM) and particle swarm optimization (PSO) algorithms. SVM training was conducted with a linear kernel, the ‘C’ value parameter was 10 resulting in an accuracy value of 97.28%. The SVM algorithm optimized using the PSO method produces an accuracy of 97.61% using the parameters c1 is 0.2, c2 is 0.5 and w is 0.6. Based on these results, sentiment analysis using PSO-based SVM optimization has been successfully carried out with an increase in accuracy of 0.33%. This game review has a sentiment value from neutral to positive so this game can be recommended to other players
Comparative analysis of heart failure prediction using machine learning models
Get PDFHeart failure is a critical health problem worldwide, and its prediction is a major challenge in medical science. Machine learning has shown great potential in predicting heart failure by analyzing large amounts of medical data. Heart failure prediction with the help of machine learning classification algorithms involves the use of models such as decision trees, logistic regression, and support vector machines to identify and analyze potential risk factors for heart failure. By analyzing large datasets containing medical and lifestyle-related variables, these models can accurately predict the likelihood of heart failure occurrence in individuals. In our research, the heart failure prediction and comparison are done using Logistic Regression, KNN, SVM, decision tree and random forest The accurate identification of high-risk individuals enables early intervention and better management of heart failure, reducing the risk of mortality and morbidity associated with this condition. Overall, machine learning algorithms play a major role in improving the accuracy of heart failure risk assessment, allowing for more personalized and effective prevention and treatment strategies
Medical X-ray images enhancement based on super resolution convolution neural network
Get PDFPneumonia is a severe lung infection, chest X-ray (CXR) image preferred to find infection. Real images lost its quality, resolution and other feature due to transmission. So good qualitative datasets are very limited. Quality enhancement in medical images is challenging task for researchers. And quality in clinical diagnosis of any disease in deep learning play a very important role. So, this paper presents an aspect with importance of quality in medical images CXR of a particular dataset and how to enhance and create new images with high quality resolution, that is re-used for classification in deep learning. Super resolution convolutional neural netwok (SRCNN) is deep learning based method, which is used for improving resolution in image. Super resolution means low resolution (LR) images from dataset is to be reconstructed or magnified into high resolution (HR). The objective behind this study is to measure the effect of super resolution with quality index, peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). This experinment performed on 200 images with 10 batches, each batch has 20 images from Kermany dataset, select LR images and converted into HR with SRCNN. Then we find PSNR value of image is increase upto 2 to 5 DB, and MSE of qood quality images is near to zero and MSE decrease up to 20-25, SSIM value have little variation due to same pattern is found in input and output images. Enhancement means highlight or improve the region of interest of pneumonic images. Main goal of this study is to preapare a modified dataset which is further used for classification
Review-based analysis of clustering approaches in a recommendation system
Get PDFBecause of the explosion in data, it is now incredibly difficult for a single person to filter through all of the information and extract what they need. As a result, information filtering algorithms are necessary to uncover meaningful information from the massive amount of data already available online. Users can benefit from recommendation systems (RSs) since they simplify the process of identifying relevant information. User ratings are incredibly significant for creating recommendations. Previously, academics relied on historical user ratings to predict future ratings, but today, consumers are paying more attention to user reviews because they contain so much relevant information about the user's decision. The proposed approach uses written testimonials to overcome the issue of doubt in the ratings' pasts. Using two data sets, we performed experimental evaluations of the proposed framework. For prediction, clustering algorithms are used with natural language processing in this strategy. It also evaluates the findings of various methods, such as the K-mean, spectral, and hierarchical clustering algorithms, and offers conclusions on which strategy is optimal for the supplied use cases. In addition, we demonstrate that the proposed technique outperforms alternatives that do not involve clustering
ChatGPT's effect on the job market: how automation affects employment in sectors using ChatGPT for customer service
Get PDFA significant language model called ChatGPT, created by OpenAI, has gained attention in artificial intelligence (AI) and natural language processing. This research paper aims to provide an in-depth analysis of ChatGPT and its potential impact on the future, including its limitations, pros and cons, and how it came to be. This paper first provides a brief overview of ChatGPT, including its architecture and training process, and how it differs from previous language models. It then delves into the model's limitations, such as its lack of common sense and susceptibility to discrimination or biases present in the data it was trained on. This paper also explores the potential benefits of ChatGPT, such as its ability to generate human-like text, its potential use in customer service, and its potential impact on the job market. The paper also discusses the ethical and social implications of ChatGPT, such as the potential for the model to perpetuate biases and the need for transparency and accountability in its deployment. Finally, the paper concludes by discussing the future of ChatGPT and similar language models and their potential impact on various industries and society as a whole. Overall, this research paper provides a comprehensive and nuanced survey of the AI tool ChatGPT and its potential impact on the future
Mobile forensics tools and techniques for digital crime investigation: a comprehensive review
Get PDFExtracting and analyzing data from smartphones, IoT devices, and drones is crucial for conducting digital crime investigations. Effective cyberattack mitigation necessitates the use of advanced Android mobile forensics techniques. The investigation necessitates proficiency in manual, logical, hex dump, chip-off, and microread methodologies. This paper provides a comprehensive overview of Android mobile forensics tools and techniques for digital crime investigation, as well as their use in gathering and analyzing evidence. Forensic software tools like Cellebrite UFED, Oxygen Forensic Detective, XRY by MSAB, Magnet AXIOM, SPF Pro by SalvationDATA, MOBILedit Forensic Express, and EnCase Forensic employ both physical and logical techniques to retrieve data from mobile devices. These advanced tools offer a structured approach to tackling digital crimes effectively. We compare dependability, speed, compatibility, data recovery accuracy, and reporting. Mobile-network forensics ensures data acquisition, decryption, and analysis success. Conclusions show that Android mobile forensics tools for digital crime investigations are diverse and have different capabilities. Mobile forensics software offers complete solutions, but new data storage and encryption methods require constant development. The continuous evolution of forensic software tools and a comprehensive tool classification system could further enhance digital crime investigation capabilities
Application of monarch butterfly optimization algorithm for solving optimal power flow
Get PDFThis paper proposes a highly flexible, robust, and efficient constraint-handling approach for the solution of the optimal power flow (OPF) problem and this solution lies in the ability to solve the power system problem and avoid the mathematical traps. Centralized control of the power system has become inevitable, in the interest of secure, reliable, and economic operation of the system. In this work, OPF is solved by considering the three distinct objectives, generation cost minimization, power loss minimization, and enhancement of voltage stability index. These three objectives are solved separately by considering the evolutionary-based monarch butterfly optimization (MBO) algorithm. This MBO algorithm is validated on the IEEE 30 bus network and the obtained results are compared with differential evolution, particle swarm optimization, genetic algorithm, and Jaya algorithm. The obtained results reveal that among the various optimization algorithms considered in this work, the MBO evolves as the best algorithm for all three case studies
Explainable artificial intelligence for traffic signal detection using LIME algorithm
Get PDFAs technology progresses, so does everything around us, such as televisions, mobile phones, and robots, which grow wiser. Of these technologies, artificial intelligence (AI) is used to aid the computer in making decisions comparable to humans, and this intelligence is supplied to the machine as a model. As AI deals with the concept of Black-Box, the model’s decisions were poorly comprehended by the end users. Explainable AI (XAI) is where humans can understand the judgments and decisions made by the AI. Earlier, the predictions made by the AI were not as easy as we know the data now, and there was some confusion regarding the predictions made by the AI. The intention for the use of XAI is to improve the user interface of products and services by helping them trust the decisions made by AI. The machine learning (ML) model White-box shows us the result that can be understood by the people in that domain, wherein the end users cannot understand the decisions. To further enhance traffic signal detection using XAI, the concept called local interpretable model- agnostic explanation (LIME) algorithm has been taken into consideration and the performance is improved in this paper
Security analysis and evaluation of mobile banking applications in Nigeria
Get PDFRapid fintech adoption across the world is so ubiquitous. To facilitate more adoption in Nigeria, recently the Central Bank of Nigeria (CBN) introduced several policies that support cashless banking. Nowadays, Nigerian banks users could perform most of their daily transactions from any desired location using mobile banking applications. In the literature, there are insufficient studies that comprehensively evaluate the security strength or risks of these applications. Generally, insecure mobile banking applications could lead to financial fraud, violations of privacy, identity theft and eroded user confidence. Considering the situation, there is need to conduct research which comprehensively assess security of the applications. Consequently, in this paper we analyzed and evaluated the security of identified popular mobile banking applications in Nigeria. We conducted the analysis work using automated and manual static analysis methods. Then, we evaluated the security of the applications using multi-criteria decision-making technique. Our results revealed that most of the applications have several security challenges in form of vulnerabilities and insecure coding practices. Hence, our findings have shown the applications need further improvements for better security and safety