IAES International Journal of Artificial Intelligence (IJ-AI)
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Hybrid channel and spatial attention-UNet for skin lesion segmentation
Get PDFMelanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710
Autism spectrum disorder identification with multi-site functional magnetic resonance imaging
Get PDFAutism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system
Machine learning for potential anti-cancer discovery from black sea cucumbers
Get PDFDespite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug
Hybrid optimal feature selection approach for internet of things based medical data analysis for prognosis
Get PDFHealthcare is very important application domain in internet of things (IoT). The aim is to provide a novel combined feature selection (FS) methods like univariate (UV) with tree-based methods (TB), recursive feature elimination (RFE) with least absolute shrinkage selection operator (LASSO), mutual information (MI) with genetic algorithm (GA) and embedded methods (EM) with univariate has been applied to internet of medical things (IoMT)based heart disease dataset. The well-suited machine learning algorithms for IoT medical data are logistic regression (LR) and support vector machine (SVM). Each combined method has been applied to the machine learning algorithms to find the best classifier for prognosis. The various performance metrices has been calculated for all the combined feature selection methods for logistic regression and support vector machine and found that for precise classification could be done using recursive elimination feature selection method with LASSO applied to logistic regression achieved a better performance than all other combined methods with high accuracy, sensitivity and high area under curve. Decision has been taken by data analytics that RFE+LASSO using LR feature selection method will provide an overall better performance for IoT based medical heart disease dataset after comparing all other combined methods with LR and SVM classifiers
Bangla song genre recognition using artificial neural network
Get PDFMusic has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, SoundCloud, or any other internet platform. Music information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, rock, hip hop, Nazrul, Rabindra, and folk music. We have started with some traditional machine learning algorithms having k-nearest neighbor, logistic regression, random forest, support vector machine, and decision tree but ended up with a deep learning algorithm named artificial neural network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and mean chroma frequency values of that raw amplitude data. But we found that music tempo having beats per minute value with two previous features present better accuracy
Optimizing pulmonary carcinoma detection through image segmentation using evolutionary algorithms
Get PDFThis paper’s goal is to suggest an image segmentation technique for use with medical images, specifically computer tomography scan images, to aid doctors in understanding the images. To address a variety of picture segmentation issues, it is necessary to investigate and apply novel evolutionary algorithms. The study focuses on pulmonary carcinoma, which is the cancer that affects males the most frequently across the globe. For proper treatment and life-saving measures, early identification of lung cancer is essential. To identify lung cancer, doctors frequently employ the computed tomography imaging technique. In order to extract tumours from lung scans, the study analyses the effectiveness of three optimization algorithms: k-means clustering, particle swarm optimization, and modified guaranteed convergence particle swarm optimization. The study also examines the pre-processing performance of four filters, namely the mean, bilateral, gaussian, and laplacian filters, shows that the bilateral filter is best suited for CT scans of the body. To test the proposed technique on 30 examples of lung scans. The proposed algorithm is tested on 30 sample lung images. The results show that the modified guaranteed convergence particle swarm optimization algorithm has the highest accuracy of 96.01%
A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems
Get PDFPublic transport is a serious problem that is difficult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta-heuristic methods, i.e. ill-climbing, simulated annealing, and hyper-heuristics method based on modified particle swarm optimization algorithm. The experimental results showed that the proposed methods could solve UTRP effectively. Regarding their performance, the results show that despite the generality of hyper-heuristics, their performance are competitive. More specifically, hyper-heuristics method is the best method compared to the other two methods in each dataset. In addition, compared to prior studies results, he proposed hyper-heuristics could outperform them in term of cost passenger of small dataset Mandl. The main contribution of this paper is that to best of our knowledge, it is the first study comparing the performance of meta-heuristics and hyper-heuristics approaches over UTRP
Fragmented-cuneiform-based convolutional neural network for cuneiform character recognition
Get PDFCuneiform has been a widely used writing system in one of the human history phases. Although there are millions of tablets, have been excavated today, only around 100,000 tablets have been read. The difficulty in translating also increased if the tablet has damaged areas resulting in some of its characters become fragmented and hard to read. This paper investigates the possibility of reading fragmented cuneiform characters from Noto Sans Cuneiform font based on convolutional neural network (CNN). The dataset is built on extracted 921 characters from the font. These characters are then intentionally being damaged with specific patterns, resulting set of fragmented characters ready to be trained. The model produced by this training phase then being used to read the unseen fragmented pattern of cuneiform sets. The model also being tested for reading normal characters set. From the simulation, 83.86% accuracy of reading fragmented characters are obtained. Interestingly, 96.42% accuracy is obtained while the model is being tested for reading normal characters
Enhancing service excellence: analyzing natural language question answering with advanced cosine similarity
Get PDFInformation related to student services in higher education must be produced and disseminated in various forms. Covid-19 pandemic, student services with a remote model related to this question and answer become very important. To carry out this automation process, the advanced cosine similarity method is used to check the similarity of the questions to the database and statistics to calculate the similarity value of each word. The proposed paper proceeds with three phases. The first stage to solve this problem is the data processed in question; the professional next step is word insertion. It converts alphanumeric words to vector format. Each word is a vector that represents a point in space with a certain dimension. The recommended advanced cosine similarity data still must be analyzed into a statistical approach. We will measure accuracy to get results so that optimal results and answers are obtained, research procedures are carried out based on literature study, initial data collection and observation, system development, system testing, system analysis, and system evaluation. This research implemented in universities with student chat automation applications providing an accuracy 83.90% given by natural language question answering system (NLQAS) so that it can improve excellent service in universities
Enhancing machine failure prediction with a hybrid model approach
Get PDFThe industrial sector is undergoing a substantial transformation by embracing predictive maintenance approaches, aiming to minimize downtime and reduce operational expenses. This transformative shift involves the incorporation of machine learning techniques to refine the accuracy of predicting machinery failures. In this article, we delve into an in-depth exploration of machine failure prediction, employing a hybrid model amalgamating long short-term memory (LSTM) and support vector machine (SVM). Our comprehensive study meticulously assesses the hybrid model’s performance, comparing it with standalone LSTM and SVM models across three distinct datasets. The results showcase that the hybrid model outperformed, providing the modest dependable, and highest F1-score values in our evaluation