From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine lea...
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ISBN:
(数字)9798350360165
ISBN:
(纸本)9798350360172
From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings.
As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is ...
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ISBN:
(数字)9798331530303
ISBN:
(纸本)9798331530310
As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability.
Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel t...
Since the beginning of the seventeenth century, wheelchairs have been used to transport hospital patients and those with mobility impairments. The power of people is what spreads wheelchairs. Wheelchair users propel themselves forward using their upper bodies or, more commonly, require the assistance of another person to propel them. In this work, we examine how to build an autonomous electric wheelchair for the disabled at the lowest possible cost without sacrificing quality of design or control. This research presents the planning and development of an AI-enhanced electric wheelchair that can be controlled automatically to climb stairs, making it accessible to those with mobility impairments. The chair’s controls allow the user to move it in any direction and raise the landing and staircases to varying heights. The chair’s DC power source (battery used) may be recharged, allowing it to function for an extended period of time. In addition, a safety system was created that monitors and provides feedback on the user’s pulse rate, as well as the wheelchair’s functioning state (including acceleration, climate, and inclination position). This proposed AI based Wheelchair (AIWC) design is suitable for any aged people and the proposed model is cross-validated with the conventional wheelchair design using mobile based operations handling system called Mobile-Wheelchair (MWC) to test the efficiency of the proposed design in clear manner.
In this report, we describe an ensemble approach with a set of enhanced random forest models for COVID-19 retweet prediction challenge at CIKM Analyticup 2020 held by the 29th ACM International Conference on Informati...
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Braille has empowered visually challenged community to read and write. But at the same time, it has created a gap due to widespread inability of non-Braille users to understand Braille scripts. This gap has fuelled re...
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This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral, probabilistic, and deep metric learning. We first start with the definition of distance metric, Mahalanobis distance, and g...
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Solving high-dimensional semilinear parabolic partial differential equations (PDEs) challenges traditional numerical methods due to the"curse of dimensionality." Deep learning, particularly through the Deep ...
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In order to keep the electric power system running smoothly and reliably, it is constantly monitored and controlled by an intelligent cyber layer that consists of data processing algorithms and a wide network of senso...
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ISBN:
(数字)9798350368949
ISBN:
(纸本)9798350368956
In order to keep the electric power system running smoothly and reliably, it is constantly monitored and controlled by an intelligent cyber layer that consists of data processing algorithms and a wide network of sensors. Cyberattacks on this data processing and collecting system might halt operations and have catastrophic physical consequences like system breakdowns. A common kind of attack is the FDIA. To avoid detection by the state estimator's inherent anomaly detection capabilities, an attacker tries to trick the grid's underlying control system into producing disturbances. On top of that, there are major issues with customer-end security. For instance, criminals might intercept, alter, or replay wireless transmissions of power consumption signals sent by consumers to their utility provider. Consequently, smart grid security and privacy are major apprehensions. In order to prevent cyberattacks on the vital infrastructures of the electrical system, this thesis helps. New detection systems for the generating and transmission side, as well as the end-user customer side infrastructure, are the primary focus of the study. To protect the generating and transmission sides from cyber-attack, we provide a system based on intelligent sensor weights and an optimization strategy based on sophisticated grey wolves. In addition, to safeguard end-user customer data from cyberattacks, we provide a gaussian mixture model–based privacy protection strategy and an artificial neural structure–based intelligent loop detection method.
Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the i...
Intensity inhomogeneity is a significant issue in magnetic resonance imaging (MRI), where the presence of bias field causes distortions in pixel values, resulting in inconsistent and erroneous intensities across the image. This artifact not only hampers accurate diagnosis by radiologists but also negatively impacts the performance of computer-aided diagnosis algorithms, particularly in tasks like segmentation. In our proposed approach, we use a hybrid technique called KIFCM, which integrates K-means and Fuzzy C-means to enhance brain tumor segmentation. K-means provides computational efficiency, while Fuzzy C-means improves accuracy by detecting missed tumor cells. We employ a bias correction method based on the level set framework, removing noise with a median filter and applying the hybrid KIFCM technique for optimal segmentation. Our method effectively addresses intensity variation challenges, ensuring precise brain tumor region segmentation. We compare our results with DFCM and MFFLs, and the comparison shows the efficiency of our proposed method by highlighting the superior quality and accuracy of 81% achieved, requiring less computational time. Consequently, our results demonstrate KIFCM's potential to boost both accuracy and speed in MRI-based brain tumor detection through computer-aided diagnosis.
We study the problem of reconfiguring one minimum s-t-separator A into another minimum s-t-separator B in some n-vertex graph G containing two non-adjacent vertices s and t. We consider several variants of the problem...
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