Inferring individualised treatment effects from observational data can unlock the potential for targeted interventions. It is, however, hard to infer these effects from observational data. One major problem that can a...
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Recently, "pre-training and fine-tuning" has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annotations from each application....
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For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, how...
For establishing a high efficient signal communications, a Millimeter-Wave-Massive-MIMO (mmWave-MMIMO) logics are helpful and more speculated. Although current hybrid precoding schemes are computationally complex, however, they are not using all of the spatial details. The combination of analog and digital precoding logics are considered to be a significant methodology to deal with complexities over hardware area and the higher consumption of energy in association with combination of signal elements. But the basic degradation of the traditional hybrid-precoding methodologies has some issues such as processing complexities and has a lacking to explore the spatial data regarding the channels. To address these limitations, this paper suggests a Novel Deep Learning Scheme (NDLS), in which it is enabled by mmWave-MMIMO system for successful Hybrid Precoder Logic (HDL). As well as each Precoder selection is used to obtain the The optimised-decoder is perceived in the Novel Deep Learning Scheme as a mapping relation. To be more precise, a Hybrid Precoder Logic is extracted via Novel Deep Learning Scheme training for optimizing the Millimeter- Wave-Massive-MIMO precoding method. Additionally, a detailed simulation results are presented to demonstrate the proposed scheme's superior efficiency and the resulting section demonstrate that the Novel Deep Learning Scheme based methodology is competent of lowering the Bit-Error- Rate (BER) and increasing the effectiveness of the frequency of Millimeter-Wave Massive-MIMO (mmWave- MMIMO), gaining a competitive advantage in faster convergence as compared to traditional schemes while significantly reducing the necessary computational difficulty.
In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases i...
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In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.
Alzheimer's disease (AD) necessitates accurate early diagnosis for effective treatment. Our study explores the untapped potential of spatial information extraction from resting-state functional magnetic resonance ...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Alzheimer's disease (AD) necessitates accurate early diagnosis for effective treatment. Our study explores the untapped potential of spatial information extraction from resting-state functional magnetic resonance imaging (rs-fMRI) and its integration with structural MRI (sMRI) for investigating AD-related brain alterations. Utilizing fMRI networks (independent component analysis followed by voxelwise intensity projections i.e., iVIP) outperforms traditional metrics, such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF, in capturing critical spatial maps for AD classification. A multi-channel convolutional neural network inspired by AlexNet dropout architecture effectively models spatial and temporal dependencies in integrated data. Experiments on the Alzheimer’s Disease Neuroimaging Initiative dataset demonstrate superior classification performance with 93.31% test accuracy and a 97.79 AUC score, surpassing existing methods. Fusion results generally outperform unimodal results, revealing significant differences in neurobiologically relevant regions. Saliency visualizations highlight distinctions in the hippocampus, amygdala, caudate nucleus, and thalamus, aligning with previous literature.
This analytical examine investigates the safety of net Key change (IKE) Protocol for wireless networks. It specializes in how IKE works and the unique additives that it involves so one can provide relaxed key trade. T...
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ISBN:
(数字)9798350395327
ISBN:
(纸本)9798350395334
This analytical examine investigates the safety of net Key change (IKE) Protocol for wireless networks. It specializes in how IKE works and the unique additives that it involves so one can provide relaxed key trade. The paper examines the IKE protocol primarily based at the maximum current requirements and gives a complete review of ways the protocol works, its key components, the safety mechanisms it is predicated on, its blessings, and its weaknesses. thru our examine, it's miles discovered that even though IKE is considered to be one of the most relaxed protocols for Wi-Fi networks, there are nevertheless certain regions within the protocol that have ability for improvement. The paper concludes with numerous tips on a way to maximize the security furnished by using IKE while minimizing its potential for weakness.
This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug mo...
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ISBN:
(数字)9798350395914
ISBN:
(纸本)9798350395921
This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.
Network security involves safeguarding and preventing unauthorized access to networks through various methodologies, including access control, firewalls, and intrusion detection and prevention systems (IDS/IPS). Despi...
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Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This...
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ISBN:
(数字)9798331541583
ISBN:
(纸本)9798331541590
Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.
Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs perf...
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Supervised Classification (SML) is the pursuit of systems that reasoning from externally given instances to generate broad hypotheses, which subsequently generate predictions for future instances. One of the jobs performed by intelligent systems most commonly is supervised classification. Based on the data set, number of occurrences and variables in this study, the most efficient classification algorithm is selected. It is comprehensively described and contrasted with various supervised learning methods. Seven distinct machine learning algorithms were taken into consideration utilizing the Waikato Environment for data Analysis (WEDA) machine learning tool. For the identification process, the data set was used with 780 instances, eight independent variables (quality), and one dependent variable (variable). According to the data, SVM was the technique with the highest degree of precision and accuracy. Accordingly, the next accurate classification algorithms after SVM were determined to be Naive Bayes and Random Forest. The study demonstrates that precision (accuracy) and model construction time are two factors, whereas kappa statistics and mean error percentage (MAE) are two other factors. Therefore, controlled predictive machine learning requires precision, accuracy, and minimal error in ML algorithms.
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