This technical article explores the evolution and current state of Natural Language Processing (NLP), focusing on its fundamental components, sentiment analysis capabilities, language generation techniques, and implementation considerations. The article examines the transformation of NLP through transformer-based architectures, discussing advancements in text preprocessing, tokenization methods, and named entity recognition. It analyzes the progression of sentiment analysis from basic lexicon-based approaches to sophisticated neural architectures, highlighting improvements in contextual understanding and emotional context detection. The article also investigates modern language generation systems, (...) their architectural innovations, and practical applications. Additionally, it addresses critical implementation considerations, including computational requirements, data quality concerns, and ethical implications, providing insights into the deployment challenges and solutions in real-world NLP applications. (shrink)

This comprehensive article explores the transformative journey of cloud migration in the modern IT landscape, examining its driving forces, benefits, challenges, and critical success factors. The article delves into how organizations across sectors increasingly adopt cloud technologies to enhance operational efficiency, reduce costs, and gain competitive advantages. It highlights the impact on healthcare organizations and discusses the strategic considerations necessary for successful migration. The article examines various aspects of cloud adoption, including financial implications, security concerns, technical challenges, and the importance (...) of proper planning and execution. Through detailed analysis of market trends and industry research, this article provides insights into the current state of cloud computing adoption and its future trajectory while offering guidance for organizations contemplating or undertaking cloud migration initiatives. (shrink)

This comprehensive article explores the transformative role of Customer Relationship Management (CRM) systems in modern healthcare delivery. The article examines how CRM platforms revolutionize patient care through enhanced engagement, streamlined operations, and data-driven decision-making capabilities. It investigates the significant impact of healthcare CRM on personalized care delivery, patient experience, and operational efficiency while addressing the technical and organizational implementation challenges. The article demonstrates how CRM systems facilitate better care coordination, improve patient outcomes, and contribute to financial sustainability in healthcare organizations. (...) Special attention is given to the unique features of healthcare CRM systems that differentiate them from traditional CRM platforms, highlighting their crucial role in advancing patient-centered care models and supporting healthcare providers in delivering more effective, efficient, personalized medical services. (shrink)

: The COVID-19 epidemic commenced in December 2019 in Wuhan, China. In contrast to the Spanish flu pandemic of 1918, the death rate from COVID-19 is merely 5%. The condition is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 strain. In reaction to this catastrophe, several nations have implemented quarantines to mitigate the worldwide dissemination of the COVID-19 virus. COVID-19 vaccines will not be accessible until December 2020. A dependable identification method is essential for the early detection of COVID-19 (...) to preserve humanity. Recent studies indicate that Chest X-ray (CXR) imaging is the most expedient way for diagnosing and classifying COVID-19, and it is also more reliable than Reverse Transcription Polymerase Chain Reaction (RT-PCR) as it yields critical information regarding the coronavirus. Clinical practitioners may initiate treatment at the preliminary stage based on this CXR diagnosis. This article proposes methods for predicting the spread of the COVID-19 pandemic utilizing GLCM-CNN, a deep learning system. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several constraints, (...) especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

Image synthesis is the process of generating new images from the ground up, frequently utilizing preexisting data or models. By definition, super-resolution methods produce supplementary image content and features that are not present in the original input in order to reconstruct a high-resolution image from a low-resolution source. Surpassing the performance attained with high-resolution images is a challenge when training or analyzing models with low-resolution images. It is not always easy to obtain a higher-resolution image. Recognizing and identifying objects in (...) low-resolution images is a challenging task. As a result, it is imperative to develop a method that simultaneously enhances the resolution of the low-resolution image and enhances its quality. Generative Adversarial Networks (GANs) and other generative models are increasingly acknowledged for their capacity to accurately replicate high-resolution counterparts. This article provides a performance comparison of DCGAN and Wasserstein GAN for image synthesis via image super resolution. The experimental endeavor employs the Set5 image data set. Wasserstein GAN outperforms DCGAN in terms of PSNR, SSIM, and VIF parameters. (shrink)

There are various aspects to Internet of Things security, such as guaranteeing the safety of both the devices and the Internet of Things networks to which they connect. Many other types of equipment, including industrial robots, smart grids, construction automation systems, entertainment gadgets, and many more, are included in this, despite the fact that they were not designed with network security in mind. When it comes to securing systems, networks, and data, IoT device security must be able to resist a (...) wide range of IoT security assaults. One of the most important issues in the field of data security is the creation of intrusion detection systems (IDSs) for the Internet of Things. Client devices (edge devices) in Federated Learning utilize local data to train the machine learning model, and then send the updated model parameters to a cloud server so that they may be aggregated (rather than raw data).This paper proposes a machine learning system that employs federated learning to detect intrusions in the IoT. The FedAVG algorithm is employed to aggregate models. Models are trained locally by nodes. The models are trained and validated using machine learning methods, including Random Forest, ID3, and Support Vector Machine. The NSL KDD data set is employed to undertake experiments. (shrink)

Quantum machine learning (QML) provides a transformative approach to data analysis by integrating the principles of quantum computing with classical machine learning methods. With the exponential growth of data and the increasing complexity of computational tasks, quantum algorithms offer tremendous advantages in terms of processing speed, memory efficiency, and the ability to resolve issues intractable for classical systems. In this work, the use of QML techniques for both supervised and unsupervised learning problems is explored. Quantum-enhanced models such Quantum Support Vector (...) Machines (QSVMs) and Quantum Neural Networks (QNNs) show outstanding performance in classification and regression tasks by using quantum kernels and entanglement in supervised learning. Moreover, hybrid quantum-classical solutions offer useful implementations on noisy intermediate-scale quantum (NISQ) devices, hence bridging the gap between present quantum technology and practical uses. By means of comparative analysis, this paper emphasizes the possible benefits and drawbacks of QML, thereby providing understanding of its future importance in sectors including material science, finance, and healthcare. In the end, QML opens the path for a new era of intelligent data processing and solves until unthinkable difficult challenges. (shrink)

In the past decade, blockchain technology has garnered significant attention from a diverse array of industries. Despite the fact that blockchain technology was initially developed for the Bitcoin P2P cryptocurrency network, there has been a recent surge in interest in its potential integration with other service sectors. In the domain of machine-to-machine commerce, distributed ledger technologies, as seen in blockchain systems, have recently been hailed as potential platforms. A decentralised network architecture, security, immutability, and transparency are among the primary attributes (...) of blockchains. The unalterable nature of blockchain ledgers enables members to rely on one another, thereby increasing performance and participation. Scalability and security are substantial challenges for blockchain technology. This study provides a comprehensive analysis of the emergence of blockchain technology and various methods for addressing the scalability issues associated with distributed ledgers. (shrink)

This intersection of Web3 technology and the metaverse constitutes a major leap into decentralized digital environments. Web3, which is based on blockchain technology, offers a user-centric architecture that allows users to own and control their digital assets, identities, and interactions. Blockchain ensures transparent, immutable records in the metaverse; smart contracts enable secure, trustless transactions to happen automatically. Non-fungible therapy tokens (NFTs) advance ownership online by showing original virtual items. Metaverse is a notion that envisions a communal, virtual shared environment that (...) exists beyond the physical world. This space is shared by all individuals. It is a digital world in which users can interact with computer-generated surroundings, connect with other users, and participate in a variety of activities by utilizing AR, VR, and other immersive technologies. This article investigates how the integration of Web3 technology supports distributed governance, virtual commerce, and interoperable experiences, thereby ushering a new era of immersive, fair digital places. We also look at potential adoption chances as well as challenges. (shrink)

Software Defined Networking (SDN) is a novel network paradigm that intelligently manages the network's resources. The global view of a network is achieved through the separation of the data plane and network control. The abstraction of the network to its application and services is facilitated by the logically centralized control plane. SDN offers the potential to enhance network performance by utilizing the information exchanged between various layers in the network architecture and feedback control, which is facilitated by a global view (...) of the network. SDN enables the network administrator to configure the network from a single point using software control. The entire network can be configured through programming and is dynamically optimized based on the network status. OpenFlow-based SDN technologies offer improved orchestration of network resources, are adaptable to evolving business requirements, and reduce operational complexities. The Network Function Virtualization (NFV) paradigm virtualizes the networking functions, similar to the hypervisors used for compute functions, and implements the entire suite of networking services as Virtual Machines (VMs). This article provides a comprehensive examination of the influence of Software-Defined Networks on data centers that are enabled by Cloud Computing. This article provides a performance comparison of ONOS and ODL data controllers on a variety of parameters, including throughput, jitter, and loss. (shrink)

The quick expansion of the Internet of Things (IoT) has produced exponential data production requiring efficient processing solutions. Because of too high latency, limited bandwidth, and security concerns, real-time applications find conventional cloud-based architectures less suitable. Edge computing addresses these restrictions by processing data nearer the source, thus reducing latency, improving response times, and so raising overall system efficiency. Edge computing—by leveraging localized computation—allows real-time decision-making for significant IoT purposes like smart cities, industrial automation, healthcare, and autonomous autos. Filtering and (...) evaluating data at the edge before passing relevant information to the cloud helps to clear network congestion as well. This study addresses the importance of edge computing in IoT, its primary benefits, challenges, and future prospects in providing intelligent, real-time, scalable IoT systems. (shrink)