KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI

In order to fully harness the potential of machine learning, it is crucial to establish a system that renders the field more accessible and less daunting for individuals who may not possess a comprehensive understanding of its intricacies. The paper describes the design of a system that integrates AutoML, XAI, and synthetic data generation to provide a great UX design for users. The system allows users to navigate and harness the power of machine learning while abstracting its complexities and providing high usability. The paper proposes two novel classifiers, Logistic Regression Forest and Support Vector Tree, for enhanced model performance, achieving 96\% accuracy on a diabetes dataset and 93\% on a survey dataset. The paper also introduces a model-dependent local interpreter called MEDLEY and evaluates its interpretation against LIME, Greedy, and Parzen. Additionally, the paper introduces LLM-based synthetic data generation, library-based data generation, and enhancing the original dataset with GAN. The findings on synthetic data suggest that enhancing the original dataset with GAN is the most reliable way to generate synthetic data, as evidenced by KS tests, standard deviation, and feature importance. The authors also found that GAN works best for quantitative datasets.Comment: 33 pages, 20 figure

Design and Modelling of Decentralised Task Allocation Mechanisms in Groups of Mobile Agents

Division of labour is a fundamental field of research within the context of multi-agent (particularly swarm based systems) and multi-robot systems. Eusocial insects, for instance ants and bees, are known to display remarkable capabilities of allocating tasks to nest mates when the colony gets perturbed by any internal and/or external factors. Proper understanding of the underlying mechanisms of division of labour among these social insects would enable more effective designing and developing of artificial swarm based systems which in turn can be used in tackling various real world problems. At the same time, a properly built model can be used to serve as a platform for the biologists to test their research hypotheses. These key benefits have been the prime motivations of this thesis. The thesis is based on the behaviour of ant colonies and especially on how they allocate tasks in different situations. The objectives of the thesis are twofold: (1) to develop an artificial simulated system that is ant-like and (2) to explore, identify, develop and analyse task allocation strategies within the realms of colony performance. The first objective of the thesis is approached by investigating the behaviour of ant colonies from the existing literature and modelling their behaviours using an agent based modelling approach. To determine whether the model has met the first objective, three questions are posed: (A) Is the emergent system scalable? (B) Is the emergent system flexible? and (C) Is the system robust? For a system to be ant-like, the system has to not only give the appearance of ant-like behaviour but also has to meet these three criteria. As a part of the second objective of the thesis, three task allocation strategies based on ant colony behaviour are proposed. Furthermore, the strategies are critically analysed to investigate the benefits of each of the strategies and also to discover under what circumstances which strategies would perform better. The research reported in this thesis is intended to provide a better understanding of the design issues of task allocation strategies thus enabling researchers to use this as a guide to design effective task allocation strategies within the concerned multi-agent systems

AIDA: Artificial intelligence based depression assessment applied to Bangladeshi students

Depression is a common psychiatric disorder that is becoming more prevalent in developing countries like Bangladesh. Depression has been found to be prevalent among youths and influences a person’s lifestyle and thought process. Unfortunately, due to the public and social stigma attached to this disease, the mental health issue of individuals are often overlooked. Early diagnosis of patients who may have depression often helps to provide effective treatment. This research aims to develop mechanisms to detect and predict depression levels and was applied to university students in Bangladesh. In this work, a questionnaire containing 106 questions has been constructed. The questions in the questionnaire are primarily of two kinds – (i) personal, and (ii) clinical. The questionnaire was distributed amongst Bangladeshi students and a total of 684 responses (aged between 19 and 35) were obtained. After appropriate consents from the participants, they were allowed to take the survey. After carefully scrutinizing the responses, 520 samples were taken into final consideration. A hybrid depression assessment scale was developed using a voting algorithm that employs eight well-known existing scales to assess the depression level of an individual. This hybrid scale was then applied to the collected samples that comprise personal information and questions from various familiar depression measuring scales. In addition, ten machine learning and two deep learning models were applied to predict the three classes of depression (normal, moderate and extreme). Five hyperparameter optimizers and nine feature selection methods were employed to improve the predictability. Accuracies of 98.08%, 94.23%, and 92.31% were obtained using Random Forest, Gradient Boosting, and CNN models, respectively. Random Forest accomplished the lowest false negatives and highest F Measure with its optimized hyperparameters. Finally, LIME, an explainable AI framework, was applied to interpret and retrace the prediction output of the machine learning models

BanglaLekha-Isolated: A multi-purpose comprehensive dataset of Handwritten Bangla Isolated characters

BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2

Mitigating carbon footprint for knowledge distillation based deep learning model compression.

Deep learning techniques have recently demonstrated remarkable success in numerous domains. Typically, the success of these deep learning models is measured in terms of performance metrics such as accuracy and mean average precision (mAP). Generally, a model's high performance is highly valued, but it frequently comes at the expense of substantial energy costs and carbon footprint emissions during the model building step. Massive emission of CO2 has a deleterious impact on life on earth in general and is a serious ethical concern that is largely ignored in deep learning research. In this article, we mainly focus on environmental costs and the means of mitigating carbon footprints in deep learning models, with a particular focus on models created using knowledge distillation (KD). Deep learning models typically contain a large number of parameters, resulting in a 'heavy' model. A heavy model scores high on performance metrics but is incompatible with mobile and edge computing devices. Model compression techniques such as knowledge distillation enable the creation of lightweight, deployable models for these low-resource devices. KD generates lighter models and typically performs with slightly less accuracy than the heavier teacher model (model accuracy by the teacher model on CIFAR 10, CIFAR 100, and TinyImageNet is 95.04%, 76.03%, and 63.39%; model accuracy by KD is 91.78%, 69.7%, and 60.49%). Although the distillation process makes models deployable on low-resource devices, they were found to consume an exorbitant amount of energy and have a substantial carbon footprint (15.8, 17.9, and 13.5 times more carbon compared to the corresponding teacher model). The enormous environmental cost is primarily attributable to the tuning of the hyperparameter, Temperature (τ). In this article, we propose measuring the environmental costs of deep learning work (in terms of GFLOPS in millions, energy consumption in kWh, and CO2 equivalent in grams). In order to create lightweight models with low environmental costs, we propose a straightforward yet effective method for selecting a hyperparameter (τ) using a stochastic approach for each training batch fed into the models. We applied knowledge distillation (including its data-free variant) to problems involving image classification and object detection. To evaluate the robustness of our method, we ran experiments on various datasets (CIFAR 10, CIFAR 100, Tiny ImageNet, and PASCAL VOC) and models (ResNet18, MobileNetV2, Wrn-40-2). Our novel approach reduces the environmental costs by a large margin by eliminating the requirement of expensive hyperparameter tuning without sacrificing performance. Empirical results on the CIFAR 10 dataset show that the stochastic technique achieves an accuracy of 91.67%, whereas tuning achieves an accuracy of 91.78%-however, the stochastic approach reduces the energy consumption and CO2 equivalent each by a factor of 19. Similar results have been obtained with CIFAR 100 and TinyImageNet dataset. This pattern is also observed in object detection classification on the PASCAL VOC dataset, where the tuning technique performs similarly to the stochastic technique, with a difference of 0.03% mAP favoring the stochastic technique while reducing the energy consumptions and CO2 emission each by a factor of 18.5

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous