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Today, humans live in the era of rapid growth in electronic devices that are based on artificial intelligence, including the significant growth in the manufacture of machines that perform intelligent human tasks to solve complex situations. Artificial intelligence will significantly influence the development of many domains, especially the medical...
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... brief, artificial intelligence is a science that tries to make the computer work in the areas in which humans work and become a large part of human life that can never be missed [52][53][54][55][56][57]. Artificial intelligence includes machine learning, deep learning, and other techniques (see Figure 1) [58][59][60][61]. Since the emergence of the COVID-19 pandemic, artificial intelligence has an influential role in tracking the spread of this pandemic, monitoring its behaviour, knowing the number of cases of infection, and helping in the manufacture of vaccines [62][63][64]. ...
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... 17 Segmented boundaries and GradCAMfrom medical images. The model's exceptional accuracy, coupled with its interpretable Grad-CAM visualizations, offers promising prospects for improving medical diagnosis and decision-making[36]. By surpassing previous benchmarks and shedding light on the model's inner workings, this study paves the way for more effective and transparent AI-based tools in the field of medical image analysis, ultimately benefiting healthcare professionals and patients alike. ...
Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model’s effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model’s decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.
... For instance, a statistical regression-based methodology was proposed to create an automated early detection system for heart disease [2]. Medical imaging has also used ML to automatically identify object attributes [3]. DNN-based techniques are garnering a lot of attention among the many ML models, especially when it comes to the analysis of large datasets. ...
In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.
... Artificial Intelligence techniques have confirmed the exciting ability to effectively identify and classify malignant tumours through the capabilities of analysing medical images (X-rays, CT scans, and MRIs) [13][14][15]. These techniques are exploited ...
Keywords Artificial intelligence Tumours Genetic Machine learning Personalized medicine A B S T R A C T In the field of medicine, artificial intelligence has become a useful tool, particularly in the diagnosis and treatment of disorders with malignant tumours. Deep learning and machine learning algorithms, for example, have significant promise for increasing the precision and effectiveness of tumour diagnosis and treatment. The importance of AI in diseases with malignant tumours is examined in this work, with particular attention paid to its function in medication discovery, therapy prediction, and medical imaging analysis. It also emphasizes the difficulties and restrictions related to the application of AI, such as problems with poor data quality, as well as the requirement for legal and moral considerations. Basically, AI offers exciting possibilities to improve personalized treatment, early detection, and research developments in oncology, but careful consideration must be given to ensure appropriate and successful integration into clinical practice.
... The growth of machine learning, especially deep learning, which is a part of it, plays a vital role in diagnosing many diseases and helping professionals discover a suitable treatment or vaccine for these diseases [6]. In healthcare, machine learning and deep learning have a tremendous ability to detect diseases while analyzing data entered by healthcare workers [7]. With the growth of medical data in recent years, there have been increased opportunities to discover the behavior of diseases, their spread, classification, and division [8]. ...
Today, the world is living in a time of epidemic diseases that spread unnaturally and infect and kill millions of people worldwide. The COVID-19 virus, which is one of the most well-known epidemic diseases currently spreading, has killed more than six million people as of May 2022. The World Health Organization (WHO) declared the 2019 coronavirus disease (COVID-19) after an outbreak of SARS-CoV-2 infection. COVID-19 is a severe and potentially fatal respiratory disease caused by the SARS-CoV-2 virus, which was first noticed at the end of 2019 in Wuhan city. Artificial intelligence plays a meaningful role in analyzing medical images and giving accurate results that serve healthcare workers, especially X-ray images, which are complex images in their interpretation. In this article, two deep convolutional neural network (DCNN) classifiers, such as Inception-v2 and VGG-16, are utilized to detect COVID-19 from a set of chest X-ray images. The dataset for this article was collected from the Kaggle platform (COVID-19 Radiography Database) and consists of images of positive and healthy people. This article concludes that the most suitable performance is the Inception-v2 classifier, which has achieved an accuracy of 97% in comparison to the VGG-16 classifier, which has achieved an accuracy of 93%.
... Finally, the IoT can help support heterogeneous infrastructure, lower building costs, and simplify organizational infrastructure complexity [17]. Given the current state of the globe, the frequency of contagious diseases such as COVID-19 is not surprising, and additional factors connected to this pandemic like its high cost, going to medical centers is challenging due to distance, the requirement for quarantine during this crucial time, and Some people find it difficult, especially the disabled and elderly, who typically have at least one chronic illness [18]. To meet the demands for long-term care and remote medical monitoring, it is essential to have a convenient, comprehensive, and computer-aided technology to lessen the financial burden and to give patients a suitable quality of life [19]. ...
Data analysis depends heavily on the gathering, reasoning, and modeling of sensor-generated data. Applications for the Internet of Things (IoT) face difficulties in studying and decoding real-time data delivered through various wireless links. A data stream tracking technique called Event Process Healthcare (EPH) is used to extract relevant information from network results for use in immediate decision-making. For the data analysis of dependable healthcare applications, an event-driven IoT architecture with an event, context, and service layer is presented in this paper. In the proposed EPH method, a new algorithm known as Cloud-based Deep Learning (CDN) is introduced, which supports both patients and the healthcare industry utilizing a combination of machine learning techniques, an intelligent cloud system, and the deep learning norms serve as the foundation. Simulation is used to obtain empirical results, and it dramatically improves healthcare parameters furthermore, the EPH technique boosted precision, cut expenses, and improved health outcomes.
... In addition, attention should be paid to artificial intelligence techniques, as they have a fundamental role in the development and growth of computer systems. These techniques had a significant and influential role in confronting the COVID-19 pandemic [24][25][26]. They proved to be powerful techniques in tracking the spread of the virus, predicting, and diagnosing cases of infection [27][28][29][30][31][32]. ...
The process of digital transformation is considered one of the most influential matters in circulation at the present time, as it seeks to integrate computer-based technologies into the public services provided by companies or institutions. To achieve digital transformation, basics and points must be established, while relying on a set of employee skills and involving customers in developing this process. Today, all governments are seeking electronic transformation by converting all public services into digital, where changes in cybersecurity must be taken into account, which constitutes a large part of the priorities of nations and companies. The vulnerability to cyberspace, the development of technologies and devices, and the use of artificial intelligence in the growth of modern applications have led to the acceleration of the digital transformation process and the utilization of its services. To adopt straightforward programs and strategies to establish cybersecurity governance that can be trusted and practical in completing tasks without hacking and tampering with data and information. In this article, the importance of cybersecurity governance is highlighted in providing safe and effective technical means that have the ability to face all threats and challenges and preserve the data of individuals in various sectors.
... 4 The classifier algorithms for tumor segmentation, such as SVMs and random forests, each include classifiers to classify the pattern and the data classification algorithms. Deep learning [5][6][7] is in semantic segmentation, object detection, and image classification. Accuracy is particularly important when classifying BT. ...
... In contrast, DBN comprises two models: RBM and multilayer Journal of Electronic Imaging 062502-6 Nov∕Dec 2023 • Vol. 32 (6) perceptron (MLP). When modelling the connectivity of the neurons, suppose there are two RBM layers and that the neurons within each layer are interconnected. ...
... Metinleri sesli okuyan, insanları ve duyguları tanımlayan bu uygulamalar makine öğrenmesine dayalı 3 boyutlu teknikleri kullanırlar. Aslında yapay zeka, COVID-19 pandemisinin ortaya çıkmasından bu yana pratikte çok popüler hale geldi ve birçok doktorun hasta verilerini analiz etmesine ve virüs ile enfeksiyon oranını belirlemesine yardımcı oldu [25][26][27]. Yapay zeka, aile içi şiddet mağdurlarına yardım etme işine girmiş ve aile içi şiddete maruz kalmış bireylerle birçok görüşme yapılarak bu tür şiddetle karşılaştıklarında daha faydalı yardım almalarına yardımcı olmuştur [28]. Bu görüşmeler, kadınların en çok öldürüldüğü ülkelerden biri olduğu için Güney Afrika'da yapıldı ve şiddetin önlenmesi konusunda onlara yardımcı olmak için yapay zeka teknikleri kullanıldı. ...
Yapay zeka teknolojileri, siber güvenlik de dahil olmak üzere birçok alanı kapsamaktadır. Siber güvenliğin temel amacı; bilişim sistemlerini bilgisayar korsanları tarafından gerçekleştirilebilecek yetkisiz erişim, verilerin silinmesi/değiştirilmesi gibi şantajlara karşı korumak ve siber saldırıları önlemektir. Bu çalışma da, yapay zeka teknolojilerinin temel çerçevesi ve kavramları vurgulanarak dijital ortamda siber güvenliğin sağlanması konusunda yapay zekanın rolü ve önemi anlatılmıştır. Sanal dünyada her geçen gün daha da karmaşıklaşan siber tehditler karşısında kullanıcıların mahremiyetini ve verilerini koruyabilmek için yapay zeka yöntemlerini kullanmanın gerekli olduğu sonucuna varılmıştır.
... The elements of digital transformation have proven to be necessary and positive elements for intelligent cities, and at the same time, they may be harmful for them, because any error may be exploited and turned into threats and penetration through electronic attacks and cybercrime. Therefore, cybersecurity is necessary to control these attacks related to the misuse of digitisation and the electronic environment, and this is related to providing appropriate infrastructure and information systems based on artificial intelligence techniques, including machine learning and deep learning [8][9][10][11][12]. These techniques study the behaviour and practices of the electronic environment and detect anomalies in the systems while detecting intrusion and not allowing entry to people who do not belong to this environment. ...
Today, most governments in the world are considering establishing smart cities that work through the use of the latest technological means. Where smart cities are considered economically, socially and environmentally sustainable cities as they have the ability to develop sustainable development, increase the quality of life of citizens, increase the efficiency of available resources and active citizen participation with confidence and quickly. Nations are looking forward to creating a more profitable future for them by employing a set of main things, which are the economy, citizens, government, mobility, environment, and health. Smart cities are one of the main pillars that promote economic development in these nations. Smart cities have appeared in Japan, the UAE and Germany, where these cities constitute an excellent future environment in which they can live and are more suitable than ordinary cities. In this report, the most critical challenges that cybersecurity faces in preserving smart cities from hacking operations will be reviewed in general. This report concluded that there is a relationship between cybersecurity and smart cities. Such cities cannot be established without providing an appropriate electronic and physical security environment to protect these cities from attack and penetration by unauthorised or unknown individuals.
... Deep learning techniques provide fast and practically error-free solutions and have the ability to create the right decisions [1] [2]. Deep learning is widely employed in making intelligent machines [3]. it is part of machine learning and is a part of artificial intelligence applications [4]. ...
A convolutional neural network is one of the deep learning architectures that has been involved in a lot of the literature, and it's incredible at work. The convolutional neural network is distinguished in its use in computer vision and graphical analysis applications. It is characterised by the actuality of one or more hidden layers that extract features in images or videos, and there is also a layer to show the effects. In this regard, the authors decided to involve the convolutional neural network algorithm to classify a few chest X-ray images of COVID-19 patients and study the behaviour of this algorithm and the effects that will be obtained at the time of training. Finally, this study concluded that the performance and practices of this algorithm are very excellent and give satisfactory effects with a perfect training time.
