Search results for: Explainable AI

Authors: Mufutau Ayinla Abdul-Yakeen

Abstract:

An analysis of the five most used numerals and a proposal for the adoption of a Universally Acceptable Numerals (UAN), came up as a result of the researchers inquisitiveses of the need for a set of numerals that is universally accepted. The researcher sought for the meaning of the first letter, “Nun”, “ن”, of the first verse of Suratul-Kalam (Chapter of the Pen), the Sixty-Eighth Chapter of the Holy Qur'an. It was observed that there was no universally accepted, economical, explainable, linkable and consistent set of numerals used by all scientists up till the moment of making this enquiry. As a theoretical paper, explanatory method is used to review five of the most used numerals (Tally Marks, Roman Figure, Hindu-Arabic, Arabic, and Chinese) and the urgent need for a universally accepted, economical, explainable, linkable and consistent set of numerals arises. The study discovers: ^., I, \, _, L, U, =, C, O, 9, and 1.; to be used as numeral 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10 respectively; as a set of universally acceptable, economical, explainable, linkable, sustainable, convertible and consistent set of numerals that originates from Islam. They can be called Islameconumerals or UAN. With UAN, everything dropped, written, drawn and/or scribbled has meaning(s) as postulated by the first verse of Qur'an 68 and everyone can easily document all figures within the shortest period. It is suggested that there should be a discipline called Numeralnomics (Study of optimum utilization of Numerals) and everybody should start using the UAN, now, in order in know their strengths and weaknesses so as to suggest a better and acceptable set of numerals for the interested readers. Similarly study can be conducted for the alphabets.

Keywords: Islameconumerals, economical, Universally Acceptable Numerals (UAN), numeralnomics.

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Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba

Abstract:

Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.

Keywords: Machine learning, healthcare, classification, explainability.

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Authors: Yong W. Foo, Lai M. Tang

Abstract:

Artificial Intelligence (AI), particularly the usage of deep neural networks for hierarchical representations from data, has found numerous complex applications across various domains, including computer vision, robotics, autonomous vehicles, and other scientific fields. However, their inherent “black box” nature can sometimes make it challenging for early researchers or school students of various levels to comprehend and trust the results they produce. Consequently, there has been a growing demand for reliable visualization tools in engineering and science education to help learners understand, trust, and explain a deep learning network. This has led to a notable emphasis on the visualization of AI in the research community in recent years. AI visualization tools are increasingly being adopted to significantly improve the comprehension of complex topics in deep learning. This paper presents an approach to empower students to actively explore the inner workings of deep neural networks by integrating the student-centered learning approach of flipped classroom models with the investigative capabilities of AI visualization tools, namely, the TensorFlow Playground, the Local Interpretable Model-agnostic Explanations (LIME), and the SHapley Additive exPlanations (SHAP), for delivering an AI education curriculum. Integrating these two factors is crucial for fostering ownership, responsibility, and critical thinking skills in the age of AI.

Keywords: Deep Learning, Explainable AI, AI Visualization, Representation Learning.

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Authors: Kullanart Obsuwan, Suluck Namchote, Natdhera Sanmanee, Kamolchanok Panishkan, Sirichai Dharmvanij

Abstract:

Humic acids (HAs) have been shown to activate some ion uptakes along with stimulating the lateral roots at effective concentration of micronutrients. However, the effects of HA on ion adsorption by plant roots are not easily explainable due to the varieties of HAs that differ from origins. Therefore, this study was aimed to investigate the effect of various concentrations of HA obtained from the compost derived from mix manures and some agricultural wastes on the growth of eggplant seedlings (Solanum melongena L. cv. Chao Praya) in tissue cultures at low nutrient level. Egg plant seeds were surfaced sterilized and germinated in ½ Murashige and Skoog medium (MS) without HA added or in ¼ MS supplemented with 0, 25, 50, 75 and 100 ppm of HAs. Then, they were cultured for 4 weeks under the controlled environment. The results showed that seedlings grown on ¼MS supplemented with HAs at the concentration of 25 and 50 ppm had the average plant heights (2.49 and 2.28 cm, respectively) higher than the other treatments. Both treatments also significantly showed the maximum average fresh and dry weights (p<0.05). Also the later yielded the highest average number of leaves and the longest average root length (p<0.05). However, there was no statistically different in the number of roots among treatments (p>0.05). This suggested that HAs at the concentration of 25 and 50 ppm could improve the growth of egg plant seedlings in tissue cultures at low nutrient level (¼ MS).

Keywords: growth, seedling, humic acid, fresh weght, dry weight, tissue culture

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