In this paper we examine the possibility of using artificial intelligence (AI) to improve academic advisement of students within the School of computing and information technology (SCIT) at the University of Technolog...
In this paper we examine the possibility of using artificial intelligence (AI) to improve academic advisement of students within the School of computing and information technology (SCIT) at the University of technology, Jamaica (Utech). Described as one of the important challenges facing academics [1], academic advisement plays a vital role in student completion. All students at Utech are assigned academic advisors and encouraged to access advisors for advisement. Each faculty manages the process internally. Students are not mandated to seek advisement but are strongly encouraged to do so to allow them to make informed choices related to module selection, academic probation, grade forgiveness, etc. Within SCIT the rate of take up is less than desired resulting in some students going on academic probation, having to switch programs in some cases or failing out of their program. We will explore the automation of the academic advisement process by using AI to push relevant information to students related to their performance. The system will be coded to recognize common situations and contact the students providing information relevant to the situation and schedule an advisement session with the academic advisor (AA).
Fog computing extends the cloud paradigm to the edge of the network, thus covering deficiencies that are in cloud computing infrastructure. Security concerns are reduced, but this does not provide a secured platform, ...
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The attention mechanism has become a pivotal component in artificial intelligence, significantly enhancing the performance of deep learning applications. However, its quadratic computational complexity and intricate c...
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The attention mechanism has become a pivotal component in artificial intelligence, significantly enhancing the performance of deep learning applications. However, its quadratic computational complexity and intricate computations lead to substantial inefficiencies when processing long sequences. To address these challenges, we introduce Attar, a resistive random access memory(RRAM)-based in-memory accelerator designed to optimize attention mechanisms through software-hardware co-optimization. Attar leverages efficient Top-k pruning and quantization strategies to exploit the sparsity and redundancy of attention matrices, and incorporates an RRAM-based in-memory softmax engine by harnessing the versatility of the RRAM crossbar. Comprehensive evaluations demonstrate that Attar achieves a performance improvement of up to 4.88× and energy saving of 55.38% over previous computing-in-memory(CIM)-based accelerators across various models and datasets while maintaining comparable accuracy. This work underscores the potential of in-memory computing to enhance the efficiency of attention-based models without compromising their effectiveness.
The integration of the Industrial Internet of Things (IIoT) brings about a significant improvement in the efficiency and productivity of industrial processes. The speed and accuracy of various tasks have been greatly ...
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Timely and precise identification of potato leaf diseases plays a critical role in improving crop productivity and reducing the impact of plant pathogens. Conventional detection techniques are often labor-intensive, d...
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Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tune...
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Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly *** identifying BAC could provide an expense,and be *** Deep Learning(DL)methods have been introduced ...
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Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly *** identifying BAC could provide an expense,and be *** Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased ***,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting *** also is not able to capture the patterns and irregularities presented in the *** solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet *** this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and *** bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher *** major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image *** bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller *** proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized *** proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).
The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted wi...
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The utilization of visual attention enhances the performance of image classification *** attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and ***-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this ***’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced *** this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention *** distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’***,a trainingmethodology is proposed to guarantee that the training problem is sufficiently *** classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the *** proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS *** obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.
People are increasingly concerned about their mental health wellness. Scientific studies suggest that online counselling for anxiety and depression is just as effective as in-person treatment. Additionally, journaling...
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Smartphones have now become an integral part of our everyday *** authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s *** technologie...
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Smartphones have now become an integral part of our everyday *** authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s *** technologies are simple,inexpensive,and fast for repeated ***,these technologies are still subject to assaults like smudge assaults and shoulder ***’touch behavior while using their cell phones might be used to authenticate them,which would solve the *** performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of *** began by concentrating on feature selection methods utilizing the grey wolf optimization *** random forest classifier is used to evaluate these *** testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public *** achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate ***,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the *** promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.
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