arXiv subjects

Md. Taimur Ahad

Publications and source records attributed to Md. Taimur Ahad.

9 recordsLinked to original sources

arXiv2026

A Novel Transfer Learning Approach for Mental Stability Classification from Voice Signal

This study presents a novel transfer learning approach and data augmentation technique for mental stability classification using human voice signals and addresses the challenges associated with limited data availability. Convolutional neural networks (CNNs) have been employed to analyse spectrogram images generated from voice recordings. Three CNN architectures, VGG16, InceptionV3, and DenseNet121, were evaluated across three experimental phases: training on non-augmented data, augmented data, and transfer learning. This proposed transfer learning approach involves pre-training models on the augmented dataset and fine-tuning them on the non-augmented dataset while ensuring strict data separation to prevent data leakage. The results demonstrate significant improvements in classification performance compared to the baseline approach. Among three CNN architectures, DenseNet121 achieved the highest accuracy of 94% and an AUC score of 99% using the proposed transfer learning approach. This finding highlights the effectiveness of combining data augmentation and transfer learning to enhance CNN-based classification of mental stability using voice spectrograms, offering a promising non-invasive tool for mental health diagnostics.

cs.SD↗

arXiv2024

A Semantic Segmentation Approach on Sweet Orange Leaf Diseases Detection Utilizing YOLO

This research introduces an advanced method for diagnosing diseases in sweet orange leaves by utilising advanced artificial intelligence models like YOLOv8 . Due to their significance as a vital agricultural product, sweet oranges encounter significant threats from a variety of diseases that harmfully affect both their yield and quality. Conventional methods for disease detection primarily depend on manual inspection which is ineffective and frequently leads to errors, resulting in delayed treatment and increased financial losses. In response to this challenge, the research utilized YOLOv8 , harnessing their proficiencies in detecting objects and analyzing images. YOLOv8 is recognized for its rapid and precise performance, while VIT is acknowledged for its detailed feature extraction abilities. Impressively, during both the training and validation stages, YOLOv8 exhibited a perfect accuracy of 80.4%, while VIT achieved an accuracy of 99.12%, showcasing their potential to transform disease detection in agriculture. The study comprehensively examined the practical challenges related to the implementation of AI technologies in agriculture, encompassing the computational demands and user accessibility, and offering viable solutions for broader usage. Moreover, it underscores the environmental considerations, particularly the potential for reduced pesticide usage, thereby promoting sustainable farming and environmental conservation. These findings provide encouraging insights into the application of AI in agriculture, suggesting a transition towards more effective, sustainable, and technologically advanced farming methods. This research not only highlights the efficacy of YOLOv8 within a specific agricultural domain but also lays the foundation for further studies that encompass a broader application in crop management and sustainable agricultural practices.

cs.CV↗

arXiv2024

A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)

Acute lymphoblastic leukaemia (ALL) is a blood malignancy that mainly affects adults and children. This study looks into the use of deep learning, specifically Convolutional Neural Networks (CNNs), for the detection and classification of ALL. Conventional techniques for ALL diagnosis, such bone marrow biopsy, are costly and prone to mistakes made by hand. By utilising automated technologies, the research seeks to improve diagnostic accuracy. The research uses a variety of pre-trained CNN models, such as InceptionV3, ResNet101, VGG19, DenseNet121, MobileNetV2, and DenseNet121, to extract characteristics from pictures of blood smears. ANOVA, Recursive Feature Elimination (RFE), Random Forest, Lasso, and Principal Component Analysis (PCA) are a few of the selection approaches used to find the most relevant features after feature extraction. Following that, machine learning methods like Naïve Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN) are used to classify these features. With an 87% accuracy rate, the ResNet101 model produced the best results, closely followed by DenseNet121 and VGG19. According to the study, CNN-based models have the potential to decrease the need for medical specialists by increasing the speed and accuracy of ALL diagnosis. To improve model performance, the study also recommends expanding and diversifying datasets and investigating more sophisticated designs such as transformers. This study highlights how well automated deep learning systems do medical diagnosis.

eess.IV↗

arXiv2023

A Comprehensive Literature Review on Sweet Orange Leaf Diseases

Sweet orange leaf diseases are significant to agricultural productivity. Leaf diseases impact fruit quality in the citrus industry. The apparition of machine learning makes the development of disease finder. Early detection and diagnosis are necessary for leaf management. Sweet orange leaf disease-predicting automated systems have already been developed using different image-processing techniques. This comprehensive literature review is systematically based on leaf disease and machine learning methodologies applied to the detection of damaged leaves via image classification. The benefits and limitations of different machine learning models, including Vision Transformer (ViT), Neural Network (CNN), CNN with SoftMax and RBF SVM, Hybrid CNN-SVM, HLB-ConvMLP, EfficientNet-b0, YOLOv5, YOLOv7, Convolutional, Deep CNN. These machine learning models tested on various datasets and detected the disease. This comprehensive review study related to leaf disease compares the performance of the models; those models' accuracy, precision, recall, etc., were used in the subsisting studies

cs.CV↗

arXiv2023

Machine Learning-Based Jamun Leaf Disease Detection: A Comprehensive Review

Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.

cs.CV↗

arXiv2023

Machine Learning-Based Tea Leaf Disease Detection: A Comprehensive Review

Tea leaf diseases are a major challenge to agricultural productivity, with far-reaching implications for yield and quality in the tea industry. The rise of machine learning has enabled the development of innovative approaches to combat these diseases. Early detection and diagnosis are crucial for effective crop management. For predicting tea leaf disease, several automated systems have already been developed using different image processing techniques. This paper delivers a systematic review of the literature on machine learning methodologies applied to diagnose tea leaf disease via image classification. It thoroughly evaluates the strengths and constraints of various Vision Transformer models, including Inception Convolutional Vision Transformer (ICVT), GreenViT, PlantXViT, PlantViT, MSCVT, Transfer Learning Model & Vision Transformer (TLMViT), IterationViT, IEM-ViT. Moreover, this paper also reviews models like Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, YOLOv5, YOLOv7, Convolutional Neural Network (CNN), Deep CNN, Non-dominated Sorting Genetic Algorithm (NSGA-II), MobileNetv2, and Lesion-Aware Visual Transformer. These machine-learning models have been tested on various datasets, demonstrating their real-world applicability. This review study not only highlights current progress in the field but also provides valuable insights for future research directions in the machine learning-based detection and classification of tea leaf diseases.

cs.CV↗

arXiv2022

Mobile phone enabled Supply chain management in the RMG sector: A conceptual framework

Relatively little is known about mobile phone use in a Supply Chain Management (SCM) context, especially in the Bangladeshi Ready-Made Garment (RMG) industry. RMG is a very important industry for the Bangladeshi economy but is criticized for long product supply times due to poor SCM. RMG requires obtaining real-time information and enhanced dynamic control, through utilizing information sharing and connecting stakeholders in garment manufacturing. However, a lack of IT support in the Bangladeshi RMG sector, the high price of computers and the low level of adoption of the computer-based internet are obstacles to providing sophisticated computer-aided SCM. Alternatively, the explosive adoption of mobile phones and continuous improvement of this technology is an opportunity to provide mobile-based SCM for the RMG sector. This research presents a mobile phone-based SCM framework for the Bangladeshi RMG sector. The proposed framework shows that mobile phone-based SCM can positively impact communication, information exchange, information retrieval and flow, coordination and management, which represent the main processes of effective SCM. However, to capitalize on these benefits, it is also important to discover the critical success factors and barriers to mobile SCM systems.

cs.HC↗

arXiv2022

IoT based SMEs shop management system

The Internet of Things (IoT) is an idea that intends to interface arranged data frameworks to actual items. The Internet of Things (IoT) has applications in pretty much every part of life in this day and age, and stock administration is no special case. IoT gives an answer for this issue by making it simpler to interface every one of the various organizations in a strategic framework utilizing Wireless Sensor Networks. An Interactive Shopping Model and an Automated Inventory Intelligent Management System that uses the Internet of Things to give constant item following, the board, and observing. A study and examination of the commonness of IoT among assembling SMEs is introduced, just as the current impediments and possibilities for permitting prescient investigation. The four examination capacities are depicted alongside an outline of the IoT empowering agents. Future patterns and difficulties in arising innovative work subjects are featured, for example, making IoT advances available to SMEs. The motivation behind this paper is to look at how the Internet of Things is changing our lives and work spaces, just as to feature probably the best strategic approaches, insights, and patterns. Considering the developing significance of big business IoT and the exploration hole in this field, an IoT design and the IoT administration industry will be examined. A model is needed to choose and send IoT administrations in different authoritative settings.

cs.HC↗

arXiv2022

Internet of Things (IoT) based Smart Agriculture Aiming to Achieve Sustainable Goals

Despite the fact, a handful of scholars have endorsed the Internet of Things (IoT) as an effective transformative tool for shifting traditional farming to smart farming, relatively little study has addressed the enabling role of smart agriculture in achieving sustainable agriculture and green climate. Researchers are more focused on technological invention and model introduction rather than discussing societal or global development goals. Sustainable development goals (SDGs) designed by United Nations (UN), therefore demand discussions as SDGs targets have a closer implication of technology. To fill this gap, in this study a model of smart agriculture is developed and centring the model we investigated how the model addresses SDGs targets. The investigation suggests that smart agriculture supports targets mentioned in Goal 6, 7, 8, 9, 11 and 12 of SDG. This research is very important, both for developing and developed nations since most of the nations are moving more towards industrialization and aiming to achieve the SDG goals This research is expected to provide a path to the IT practitioners, governments and developing agencies on how technological intervention can provide a more sustainable agricultural world.

cs.CY↗