30 research outputs found
SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification
No full textAccurate classification of brain tumors is crucial for informing clinical diagnoses and guiding patient treatment plans. It is one of the most aggressive tumors, leading to a short life expectancy. However, the classification of brain tumors is a challenging task due to the heterogeneity, complexity, and variability of brain tumors. In this work, we propose Superimposed AlexNet (SAlexNet-1 and its extension SAlexNet-2) to detect the malignancy of primary brain tumors (Glioma, Meningioma, and Pituitary) by incorporating three enhancements: (1) fusing Hybrid Attention Mechanism (HAM), (2) dense feature extraction by replacing initial convolution 11 × 11 layer with multiple convolution 3 × 3 layers for extra non-linearity alleviating parameter burden, and (3) pretraining the encoder path on a correlated dataset via semi-transfer learning (STL) enhancing model performance. HAM provides more comprehensive and accurate feature representations. In this study, we evaluated the performance of our proposed SAlexNet models on two publicly available extensive datasets for multi-class and binary classification tasks. Our results show that SAlexNet-1 achieved an accuracy of (98.78 ± 0.80 %) and (98.07± 0.02 %) on the multi-class and binary classification datasets, respectively. In comparison, SAlexNet-2 achieved outstanding accuracy of (99.69 ± 0.22 %) and (99.17 ± 0.00 %) on the multi-class and binary classification MRI datasets, respectively. The STL-based SAlexNet-2 surpassed previous literature with complex models and techniques, achieving an accuracy of (99.20 ± 0.01 %). Furthermore, we provided a comprehensive analysis of current state-of-the-art tumor classification methods on the same dataset, demonstrating the effectiveness of our approach. Enhanced tumor classification accuracy enables better diagnosis, treatment planning, and patient outcomes
Les Rituels soufis à Multan du XIXe siècle à nos jours : une étude du Sharh Mufaṣṣal Qawl-i Faṣl fī al-Bayʿa wa al-Samāʿ de Maulānā ʿUbaydullāh Multānī (m. 1305/1888) et son héritage aujourd’hui
No full textThe present study discusses the Sufi rituals in Multan performed by the Chishtiyya ʿUbaydiyya order from the 19th century to the present. Drawing from a treatise written by ʿUbaydullāh Multānī, the eponymous master, the study explores both bayʿa (initiation ritual) and samāʿ (spiritual concert) from the doctrinal perspective of the author and from the practical point of view of contemporary Chishtī ʿUbaydī shaykhs. I discuss in detail ʿUbaydullāh’s concept of both bayʿa and samāʿ according to his treatise. In order to do so, I strove to restore the text of Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ by a comprehensive study of its manuscript. Sharḥ Mufaṣṣal is one of the most significant works of ʿUbaydullāh in which he tried to reinterpret the rules and regulations of bayʿa and samāʿ in a way that integrates sharīʿa with ṭarīqat.La présente étude traite des rituels soufis à Multan pratiqués par l’ordre Chishtiyya ʿUbaydiyya à partir du 19ème siècle jusqu’à nos jours. S'appuyant sur un traité composé par ʿUbaydullāh Multānī, le maître éponyme, notre étude explore à la fois le bayʿa (rituel d'initiation) et le samāʿ (concert spirituel) selon la perspective doctrinale de l'auteur et du point de vue pratique des shaykhs chishtis ʿUbaydī contemporains. Nous analysons en détail la conception que se fait ʿUbaydullāh du bayʿa et du samāʿ dans son traité. Pour ce faire, nous nous sommes efforcés de restaurer le texte Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ par une étude approfondie de son manuscrit. Le Sharḥ Mufaṣṣal est l'une des œuvres les plus significatives de ʿUbaydullāh dans laquelle il tente de réinterpréter les règles et règlements de bayʿa et de samā de manière à intégrer la sharīʿa à la ṭarīqat
Les Rituels soufis à Multan du XIXe siècle à nos jours : une étude du Sharh Mufaṣṣal Qawl-i Faṣl fī al-Bayʿa wa al-Samāʿ de Maulānā ʿUbaydullāh Multānī (m. 1305/1888) et son héritage aujourd’hui
No full textThe present study discusses the Sufi rituals in Multan performed by the Chishtiyya ʿUbaydiyya order from the 19th century to the present. Drawing from a treatise written by ʿUbaydullāh Multānī, the eponymous master, the study explores both bayʿa (initiation ritual) and samāʿ (spiritual concert) from the doctrinal perspective of the author and from the practical point of view of contemporary Chishtī ʿUbaydī shaykhs. I discuss in detail ʿUbaydullāh’s concept of both bayʿa and samāʿ according to his treatise. In order to do so, I strove to restore the text of Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ by a comprehensive study of its manuscript. Sharḥ Mufaṣṣal is one of the most significant works of ʿUbaydullāh in which he tried to reinterpret the rules and regulations of bayʿa and samāʿ in a way that integrates sharīʿa with ṭarīqat.La présente étude traite des rituels soufis à Multan pratiqués par l’ordre Chishtiyya ʿUbaydiyya à partir du 19ème siècle jusqu’à nos jours. S'appuyant sur un traité composé par ʿUbaydullāh Multānī, le maître éponyme, notre étude explore à la fois le bayʿa (rituel d'initiation) et le samāʿ (concert spirituel) selon la perspective doctrinale de l'auteur et du point de vue pratique des shaykhs chishtis ʿUbaydī contemporains. Nous analysons en détail la conception que se fait ʿUbaydullāh du bayʿa et du samāʿ dans son traité. Pour ce faire, nous nous sommes efforcés de restaurer le texte Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ par une étude approfondie de son manuscrit. Le Sharḥ Mufaṣṣal est l'une des œuvres les plus significatives de ʿUbaydullāh dans laquelle il tente de réinterpréter les règles et règlements de bayʿa et de samā de manière à intégrer la sharīʿa à la ṭarīqat
Sufi Rituals in Multan from the 19th century to Present : a Study of Mawlānā ‘Ubaydullāh Multānī’s (d. 1305/1888) Sharh Mufaṣṣal Qawl-i Faṣl fī al-Bay‘a wa al-Samā‘ and its Legacy Today
No full textLa présente étude traite des rituels soufis à Multan pratiqués par l’ordre Chishtiyya ʿUbaydiyya à partir du 19ème siècle jusqu’à nos jours. S'appuyant sur un traité composé par ʿUbaydullāh Multānī, le maître éponyme, notre étude explore à la fois le bayʿa (rituel d'initiation) et le samāʿ (concert spirituel) selon la perspective doctrinale de l'auteur et du point de vue pratique des shaykhs chishtis ʿUbaydī contemporains. Nous analysons en détail la conception que se fait ʿUbaydullāh du bayʿa et du samāʿ dans son traité. Pour ce faire, nous nous sommes efforcés de restaurer le texte Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ par une étude approfondie de son manuscrit. Le Sharḥ Mufaṣṣal est l'une des œuvres les plus significatives de ʿUbaydullāh dans laquelle il tente de réinterpréter les règles et règlements de bayʿa et de samā de manière à intégrer la sharīʿa à la ṭarīqat.The present study discusses the Sufi rituals in Multan performed by the Chishtiyya ʿUbaydiyya order from the 19th century to the present. Drawing from a treatise written by ʿUbaydullāh Multānī, the eponymous master, the study explores both bayʿa (initiation ritual) and samāʿ (spiritual concert) from the doctrinal perspective of the author and from the practical point of view of contemporary Chishtī ʿUbaydī shaykhs. I discuss in detail ʿUbaydullāh’s concept of both bayʿa and samāʿ according to his treatise. In order to do so, I strove to restore the text of Sharḥ Mufaṣṣal Qawl-i faṣl fi l-Bayʿa wa-l-Samāʿ by a comprehensive study of its manuscript. Sharḥ Mufaṣṣal is one of the most significant works of ʿUbaydullāh in which he tried to reinterpret the rules and regulations of bayʿa and samāʿ in a way that integrates sharīʿa with ṭarīqat
Circular economy and critical barriers: Mapping the pathways and success metrics for sustainable circular success in industrialised South Asian developing nations
No full textThe economy of a developing country is often industry centric, and is recognised globally. The circular economy (CE) model involves a manufacturing approach where products are systematically remanufactured and recycled within facilities to minimize waste. The emerging domain of CE in industrialised South Asian economies offers considerable benefits for developing countries due to its sustainable production, although its adoption is obstructed by various barriers. This research aims to develop a model for adopting CE within the industrial sector of South Asian developing nations by analysing the interrelation between overcoming circular economy adoption barriers (CEABs) and sustainable circular success (SCS). To attain this, questionnaire was completed by 310 industrial experts in Pakistan. Furthermore, a partial least square structural equation modelling (PLS-SEM) approach was employed to specify the barriers and inspect the interrelation between overcoming CEABs and SCS. The findings revealed a high correlation, with addressing the CEABs contributing 66.1 % to the SCS of the industrial sector. The results of this study indicate the outer loadings and average variance extracted values for all constructs surpassed the minimum threshold of 0.5, validating their acceptance. Moreover, the constructs in the study were measured reliably, exceeding a value of 0.8, which signify strong internal consistency. Additionally, the average path coefficient value (β) of 0.172 indicates a medium and positive correlation between CEABs and SCS. The study's findings can be used as a reference for policymakers to explore the primary barriers to CE adoption in developing nations and achieve SCS in industrial projects.Full Tex
Breast Cancer Detection using Mammography: Image Processing to Deep Learning
No full textBreast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography review paper comprehensively reviews computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The review is meticulously carried out, adhering closely to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are comprehensively reviewed. Evaluating a deep learning architecture based on self-extracted features for classification tasks demonstrated outstanding performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations’ sustainability development goals
A Novel Feature-Selection Algorithm in IoT Networks for Intrusion Detection
No full textThe Internet of Things (IoT) and network-enabled smart devices are crucial to the digitally interconnected society of the present day. However, the increased reliance on IoT devices increases their susceptibility to malicious activities within network traffic, posing significant challenges to cybersecurity. As a result, both system administrators and end users are negatively affected by these malevolent behaviours. Intrusion-detection systems (IDSs) are commonly deployed as a cyber attack defence mechanism to mitigate such risks. IDS plays a crucial role in identifying and preventing cyber hazards within IoT networks. However, the development of an efficient and rapid IDS system for the detection of cyber attacks remains a challenging area of research. Moreover, IDS datasets contain multiple features, so the implementation of feature selection (FS) is required to design an effective and timely IDS. The FS procedure seeks to eliminate irrelevant and redundant features from large IDS datasets, thereby improving the intrusion-detection system’s overall performance. In this paper, we propose a hybrid wrapper-based feature-selection algorithm that is based on the concepts of the Cellular Automata (CA) engine and Tabu Search (TS)-based aspiration criteria. We used a Random Forest (RF) ensemble learning classifier to evaluate the fitness of the selected features. The proposed algorithm, CAT-S, was tested on the TON_IoT dataset. The simulation results demonstrate that the proposed algorithm, CAT-S, enhances classification accuracy while simultaneously reducing the number of features and the false positive rate.publishedVersio
Advanced Human Pose Estimation and Event Classification Using Context-Aware Features and XGBoost Classifier
No full textThis paper presents an advanced approach to Human Pose Estimation (HPE) and Semantic Event Classification (SEC), emphasizing the need for sophisticated human skeleton models, context-aware feature extraction, and machine learning techniques for precise event recognition in daily life logs. HPE, crucial in applications like sports analysis and surveillance systems, involves predicting human joint locations from images and videos. Recent deep learning advancements have significantly improved HPE, particularly in crowded scenes and occlusion challenges. Despite many surveys, a comprehensive review of HPE, especially with recent deep learning innovations, is still needed. Our research addresses this by proposing a novel HPE and SEC system. The system begins with preprocessing steps, including converting videos into image sequences, applying sliding window techniques, and converting images to grayscale, then extracting human silhouettes using binary masks. We use the GrabCut algorithm for human detection and perform skeletonization with Hough transform algorithm. Keypoint detection is achieved through pose estimation, and full-body feature extraction includes using OpenPose for movable body parts, the Lucas-Kanade method for a 3D Cartesian view, and Texton Map techniques. Key point features are further characterized using motion histograms, pose landmark visualization and Local Intensity Order Pattern (LIOP) features. The system is optimized with adaptive moment estimations and classified using the XGBoost Classifier. Evaluation on the COCO, UCF50, and YouTube datasets showed classification accuracies of 92.90%, 90.9%, and 91.2%, respectively, demonstrating our approach’s superior performance and effectiveness compared to existing state-of-the-art techniques
Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique
No full textDeep learning and artificial intelligence offer promising tools for improving the accuracy and efficiency of diagnosing various lung conditions using portable chest x-rays (CXRs). This study explores this potential by leveraging a large dataset containing over 6,000 CXR images from publicly available sources. These images encompass COVID-19 cases, normal cases, and patients with viral or bacterial pneumonia. The research proposes a novel approach called "Enhancing COVID Prediction with ESN-MDFS" that utilizes a combination of an Extreme Smart Network (ESN) and a Mean Dropout Feature Selection Technique (MDFS). This study aimed to enhance multi-class lung condition detection in portable chest X-rays by combining static texture features with dynamic deep learning features extracted from a pre-trained VGG-16 model. To optimize performance, preprocessing, data imbalance, and hyperparameter tuning were meticulously addressed. The proposed ESN-MDFS model achieved a peak accuracy of 96.18% with an AUC of 1.00 in a six-fold cross-validation. Our findings demonstrate the model’s superior ability to differentiate between COVID-19, bacterial pneumonia, viral pneumonia, and normal conditions, promising significant advancements in diagnostic accuracy and efficiency.publishedVersio
IoT-Based Multisensors Fusion for Activity Recognition via Key Features and Hybrid Transfer Learning
No full textHuman activity recognition (HAR) has attracted significant attention in various fields, including healthcare, smart homes, and human-computer interaction. Accurate HAR can enhance user experience, provide critical health insights, and enable sophisticated context-aware applications. This paper presents a comprehensive system for HAR utilizing both RGB videos and inertial measurement unit (IMU) sensor data. The system employs a multi-stage processing pipeline involving preprocessing, segmentation, feature extraction, and classification to achieve high accuracy in activity recognition. In the preprocessing stage, frames are extracted from RGB videos, and IMU sensor data undergoes denoising. The segmentation phase applies Naive Bayes segmentation for video frames and Hamming windows for sensor data to prepare them for feature extraction. Key features are extracted using techniques such as ORB (Oriented FAST and Rotated BRIEF), MSER (Maximally Stable Extremal Regions), DFT (Discrete Fourier Transform), and KAZE for image data, and LPCC (Linear Predictive Cepstral Coefficients), PSD (Power Spectral Density), AR Coefficient, and entropy for sensor data. Feature fusion is performed using Linear Discriminant Analysis (LDA) to create a unified feature set, which is then classified using ResNet50 (Residual Neural Network) to recognize activities such as using a smartphone, cooking, and reading a newspaper. The system was evaluated using the LARa and HWU-USP datasets, achieving classification accuracies of 92% and 93%, respectively. These results demonstrate the robustness and effectiveness of the proposed HAR system in diverse scenarios
