Search Results (23)

Intrusion Detection Systems (IDSs) play a crucial role in safeguarding network infrastructures from cyber threats and ensuring the integrity of highly sensitive data. Conventional IDS technologies, although successful in achieving high levels of accuracy, frequently encounter substantial model bias. This bias is primarily caused by imbalances in the data and the lack of relevance of certain features. This study aims to tackle these challenges by proposing an advanced machine learning (ML) based IDS that minimizes misclassification errors and corrects model bias. As a result, the predictive accuracy and generalizability of the IDS are significantly improved. The proposed system employs advanced feature selection techniques, such as Recursive Feature Elimination (RFE), sequential feature selection (SFS), and statistical feature selection, to refine the input feature set and minimize the impact of non-predictive attributes. In addition, this work incorporates data resampling methods such as Synthetic Minority Oversampling Technique and Edited Nearest Neighbor (SMOTE_ENN), Adaptive Synthetic Sampling (ADASYN), and Synthetic Minority Oversampling Technique–Tomek Links (SMOTE_Tomek) to address class imbalance and improve the accuracy of the model. The experimental results indicate that our proposed model, especially when utilizing the random forest (RF) algorithm, surpasses existing models regarding accuracy, precision, recall, and F Score across different data resampling methods. Using the ADASYN resampling method, the RF model achieves an accuracy of 99.9985% for botnet attacks and 99.9777% for Man-in-the-Middle (MITM) attacks, demonstrating the effectiveness of our approach in dealing with imbalanced data distributions. This research not only improves the abilities of IDS to identify botnet and MITM attacks but also provides a scalable and efficient solution that can be used in other areas where data imbalance is a recurring problem. This work has implications beyond IDS, offering valuable insights into using ML techniques in complex real-world scenarios. Full article

Microplastic pollution has become a global concern, with potential negative impacts on various ecosystems and wildlife species. Among these species, ducks (Anas platyrhynchos) are particularly vulnerable due to their feeding habits and proximity to aquatic environments contaminated with microplastics. The current study was designed to monitor microplastic (MP) pollutants in the freshwater ecosystem of the Panjkora River, Lower Dir, Pakistan. A total of twenty (20) duck samples were brought up for four months and 13 days on the banks of the river, with no food intake outside the river. When they reached an average weight of 2.41 ± 0.53 kg, all samples were sacrificed, dissected, and transported in an ice box to the laboratory for further analysis. After sample preparation, such as digestion with 10% potassium hydroxide (KOH), density separation, filtration, and identification, the MP content was counted. A total of 2033 MP particles were recovered from 20 ducks with a mean value of 44.6 ± 15.8 MPs/crop and 57.05 ± 18.7 MPs/gizzard. MPs detected in surface water were 31.2 ± 15.5 MPs/L. The major shape types of MPs recovered were fragments in crop (67%) and gizzard (58%) samples and fibers in surface water (56%). Other types of particles recovered were fibers, sheets, and foams. The majority of these detected MP particles were in the size range of 300–500 µm (63%) in crops, and 50–150 µm (55%) in gizzards, while in water samples the most detected particles were in the range of 150–300 µm (61%). Chemical characterization by FTIR found six types of polymers. Low-density polyethylene (LDPE) had the greatest polymer detection rate (39.2%), followed by polyvinyl chloride (PVC) (28.3%), high-density polyethylene (HDPE) (22.7%), polystyrene (6.6%), co-polymerized polypropylene (2.5%), and polypropylene homopolymer (0.7%). This study investigated the presence of microplastics in the crops and gizzards of ducks, as well as in river surface water. The results revealed the significant and pervasive occurrence of microplastics in both the avian digestive systems and the surrounding water environment. These findings highlight the potential threat of microplastic pollution to wildlife and ecosystems, emphasizing the need for further research and effective mitigation strategies to address this pressing environmental concern. Full article

This study’s main goal is to create a useful software application for finding and classifying fine art photos in museums and art galleries. There is an increasing need for tools to swiftly analyze and arrange art collections based on their artistic styles as a result of the digitization of art collections. To increase the accuracy of the style categorization, the suggested technique involves two parts. The input image is split into five sub-patches in the first stage. A DCNN that has been particularly trained for this task is then used to classify each patch individually. A decision-making module using a shallow neural network is part of the second phase. Probability vectors acquired from the first-phase classifier are used to train this network. The results from each of the five patches are combined in this phase to deduce the final style classification for the input image. One key advantage of this approach is employing probability vectors rather than images, and the second phase is trained separately from the first. This helps compensate for any potential errors made during the first phase, improving accuracy in the final classification. To evaluate the proposed method, six various already-trained CNN models, namely AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and InceptionV3, were employed as the first-phase classifiers. The second-phase classifier was implemented as a shallow neural network. By using four representative art datasets, experimental trials were conducted using the Australian Native Art dataset, the WikiArt dataset, ILSVRC, and Pandora 18k. The findings showed that the recommended strategy greatly surpassed existing methods in terms of style categorization accuracy and precision. Overall, the study assists in creating efficient software systems for analyzing and categorizing fine art images, making them more accessible to the general public through digital platforms. Using pre-trained models, we were able to attain an accuracy of 90.7. Our model performed better with a higher accuracy of 96.5 as a result of fine-tuning and transfer learning. Full article

In recent times, there has been considerable focus on harnessing artificial intelligence (AI) for medical image analysis and healthcare purposes. In this study, we introduce CADFU (Computer-Aided Diagnosis System for Foot Ulcers), a pioneering diabetic foot ulcer diagnosis system. The primary objective of CADFU is to detect and segment ulcers and similar chronic wounds in medical images. To achieve this, we employ two distinct algorithms. Firstly, DHuNeT, an innovative Dual-Phase Hyperactive UNet, is utilized for the segmentation task. Second, we used YOLOv8 for the task of detecting wounds. The DHuNeT autoencoder, employed for the wound segmentation task, is the paper’s primary and most significant contribution. DHuNeT is the combination of sequentially stacking two UNet autoencoders. The hyperactive information transmission from the first UNet to the second UNet is the key idea of DHuNeT. The first UNet feeds the second UNet the features it has learned, and the two UNets combine their learned features to create new, more accurate, and effective features. We achieve good performance measures, especially in terms of the Dice co-efficient and precision, with segmentation scores of 85% and 92.6%, respectively. We obtain a mean average precision (mAP) of 86% in the detection task. Future hospitals could quickly monitor patients’ health using the proposed CADFU system, which would be beneficial for both patients and doctors. Full article

Diabetic foot sores (DFS) are serious diabetic complications. The patient’s weakened neurological system damages the tissues of the foot’s skin, which results in amputation. This study aims to validate and deploy a deep learning-based system for the automatic classification of abrasion foot sores (AFS) and ischemic diabetic foot sores (DFS). We proposed a novel model combining convolutional neural network (CNN) capabilities with Vgg-19. The proposed method utilized two benchmark datasets to classify AFS and DFS from the patient’s foot. A data augmentation technique was used to enhance the accuracy of the training. Moreover, image segmentation was performed using UNet++. We tested and evaluated the proposed model’s classification performance against two well-known pre-trained classifiers, Inceptionv3 and MobileNet. The proposed model classified AFS and ischemia DFS images with an accuracy of 99.05%, precision of 98.99%, recall of 99.01%, MCC of 0.9801, and f1 score of 99.04%. Furthermore, the results of statistical evaluations using ANOVA and Friedman tests revealed that the proposed model exhibited a remarkable performance. The proposed model achieved an excellent performance that assist medical professionals in identifying foot ulcers. Full article

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data. Full article

Brain tumors (BTs) are an uncommon but fatal kind of cancer. Therefore, the development of computer-aided diagnosis (CAD) systems for classifying brain tumors in magnetic resonance imaging (MRI) has been the subject of many research papers so far. However, research in this sector is still in its early stage. The ultimate goal of this research is to develop a lightweight effective implementation of the U-Net deep network for use in performing exact real-time segmentation. Moreover, a simplified deep convolutional neural network (DCNN) architecture for the BT classification is presented for automatic feature extraction and classification of the segmented regions of interest (ROIs). Five convolutional layers, rectified linear unit, normalization, and max-pooling layers make up the DCNN’s proposed simplified architecture. The introduced method was verified on multimodal brain tumor segmentation (BRATS 2015) datasets. Our experimental results on BRATS 2015 acquired Dice similarity coefficient (DSC) scores, sensitivity, and classification accuracy of 88.8%, 89.4%, and 88.6% for high-grade gliomas. When it comes to segmenting BRATS 2015 BT images, the performance of our proposed CAD framework is on par with existing state-of-the-art methods. However, the accuracy achieved in this study for the classification of BT images has improved upon the accuracy reported in prior studies. Image classification accuracy for BRATS 2015 BT has been improved from 88% to 88.6%. Full article

Fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), is a polyphagous insect pest of many important crops. To evaluate the influence of host plants on the biology and survival of the Pakistani population of S. frugiperda, we examined life table parameters of S. frugiperda raised on maize, sorghum, wheat, and rice. The development rate was significantly higher on the maize crop than on the other three host plants. Different larval diets affected development time and fecundity. S. frugiperda attained the fastest larval development (16 days) on maize and the slowest development (32.74 days) on rice. Adult females from maize-fed larvae laid 1088 eggs/female, those from sorghum-fed larvae laid 591.6 eggs/female, those from wheat-fed larvae laid 435.6 eggs/female, and those from rice-fed larvae laid 49.6 eggs/female. Age stage-specific parameters also indicated the higher fecundity, higher life expectancy, and higher survival of S. frugiperda on maize plants than on the other three hosts. Larval diets had a significant varying effect on the finite and intrinsic increase rates, reflecting that maize was the most suitable diet. The findings of the present study are useful for predicting population dynamics especially in areas cultivating Poaceae crops, except maize, to develop sustainable integrated pest management strategies for this pest. Full article

In the present study, a numerical analysis has been conducted to investigate the hydrodynamic and thermal energy transfer capacity of a vortex formed under the effect of gravity. For this purpose, the study uses a gravitational water vortex heat exchanger (GWVHE), which includes baffles around a cylindrical basin in which a water vortex is formed under the effect of gravity. The results have been examined for different inlet boundary conditions based on flow and temperature to determine the strength of vortex formation and comparative energy transfer rate for both fluid domains. A strong vortex is formed in the basin at a height to diameter ratio between 0.41 and 0.54 with a minimum inlet mass flow rate of 0.005 kg/s, which effectively increases the energy exchange potential due to the centrifugal effect. The reasonable energy agreement has been obtained for the minimum flow rates of both fluid domains; however, the thermal energy losses are increased with the increase in the inlet mass rate of the hot domain, due to the reduction in the time of contact. The existence of an acceptable energy balance and strong vortex formation at a minimum flow rate sparks the need for a new configuration to enhance the thermal performance of GWVHE. Full article

With the increase in users of social media websites such as IMDb, a movie website, and the rise of publicly available data, opinion mining is more accessible than ever. In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. Connotation words have a different meaning than their literal meanings. While representing a word, the context in which the word is used changes the semantics of words. In this research work, categorizing movie reviews with good F-Measure scores has been investigated with Word2Vec and three different aspects of proposed features have been inspected. First, psychological features are extracted from reviews positive emotion, negative emotion, anger, sadness, clout (confidence level) and dictionary words. Second, readablility features are extracted; the Automated Readability Index (ARI), the Coleman Liau Index (CLI) and Word Count (WC) are calculated to measure the review’s understandability score and their impact on review classification performance is measured. Lastly, linguistic features are also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting 50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualized embedding of words into vectors with 50, 100, 150 and 300 dimensions.The pretrained Word2Vec model converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning (ML) algorithms are applied and evaluated according to performance measures: accuracy, precision, recall and F-Measure. The results indicate Support Vector Machine (SVM) using self-trained Word2Vec achieved 86% F-Measure and using psychological, linguistic and readability features with concatenation of Word2Vec features SVM achieved 87.93% F-Measure. Full article

Home healthcare agencies (HHCAs) provide clinical care and rehabilitation services to patients in their own homes. The organization’s rules regulate several connected practitioners, doctors, and licensed skilled nurses. Frequently, it monitors a physician or licensed nurse for the facilities and keeps track of the health histories of all clients. HHCAs’ quality of care is evaluated using Medicare’s star ratings for in-home healthcare agencies. The advent of technology has extensively evolved our living style. Online businesses’ ratings and reviews are the best representatives of organizations’ trust, services, quality, and ethics. Using data mining techniques to analyze HHCAs’ data can help to develop an effective framework for evaluating the finest home healthcare facilities. As a result, we developed an automated predictive framework for obtaining knowledge from patients’ feedback using a combination of statistical and machine learning techniques. HHCAs’ data contain twelve performance characteristics that we are the first to analyze and depict. After adequate pattern recognition, we applied binary and multi-class approaches on similar data with variations in the target class. Four prominent machine learning models were considered: SVM, Decision Tree, Random Forest, and Deep Neural Networks. In the binary class, the Deep Neural Network model presented promising performance with an accuracy of 97.37%. However, in the case of multiple class, the random forest model showed a significant outcome with an accuracy of 91.87%. Additionally, variable significance is derived from investigating each attribute’s importance in predictive model building. The implications of this study can support various stakeholders, including public agencies, quality measurement, healthcare inspectors, and HHCAs, to boost their performance. Thus, the proposed framework is not only useful for putting valuable insights into action, but it can also help with decision-making. Full article

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis, as a minor human judgment can eventually cause severe consequences. Magnetic Resonance Image (MRI) serves as a non-invasive tool to detect the presence of a tumor. However, Rician noise is inevitably instilled during the image acquisition process, which leads to poor observation and interferes with the treatment. Computer-Aided Diagnosis (CAD) systems can perform early diagnosis of the disease, potentially increasing the chances of survival, and lessening the need for an expert to analyze the MRIs. Convolutional Neural Networks (CNN) have proven to be very effective in tumor detection in brain MRIs. There have been multiple studies dedicated to brain tumor classification; however, these techniques lack the evaluation of the impact of the Rician noise on state-of-the-art deep learning techniques and the consideration of the scaling impact on the performance of the deep learning as the size and location of tumors vary from image to image with irregular shape and boundaries. Moreover, transfer learning-based pre-trained models such as AlexNet and ResNet have been used for brain tumor detection. However, these architectures have many trainable parameters and hence have a high computational cost. This study proposes a two-fold solution: (a) Multi-Scale CNN (MSCNN) architecture to develop a robust classification model for brain tumor diagnosis, and (b) minimizing the impact of Rician noise on the performance of the MSCNN. The proposed model is a multi-class classification solution that classifies MRIs into glioma, meningioma, pituitary, and non-tumor. The core objective is to develop a robust model for enhancing the performance of the existing tumor detection systems in terms of accuracy and efficiency. Furthermore, MRIs are denoised using a Fuzzy Similarity-based Non-Local Means (FSNLM) filter to improve the classification results. Different evaluation metrics are employed, such as accuracy, precision, recall, specificity, and F1-score, to evaluate and compare the performance of the proposed multi-scale CNN and other state-of-the-art techniques, such as AlexNet and ResNet. In addition, trainable and non-trainable parameters of the proposed model and the existing techniques are also compared to evaluate the computational efficiency. The experimental results show that the proposed multi-scale CNN model outperforms AlexNet and ResNet in terms of accuracy and efficiency at a lower computational cost. Based on experimental results, it is found that our proposed MCNN2 achieved accuracy and F1-score of 91.2% and 91%, respectively, which is significantly higher than the existing AlexNet and ResNet techniques. Moreover, our findings suggest that the proposed model is more effective and efficient in facilitating clinical research and practice for MRI classification. Full article

Patients who have Alzheimer’s disease (AD) pass through several irreversible stages, which ultimately result in the patient’s death. It is crucial to understand and detect AD at an early stage to slow down its progression due to the non-curable nature of the disease. Diagnostic techniques are primarily based on magnetic resonance imaging (MRI) and expensive high-dimensional 3D imaging data. Classic methods can hardly discriminate among the almost similar pixels of the brain patterns of various age groups. The recent deep learning-based methods can contribute to the detection of the various stages of AD but require large-scale datasets and face several challenges while using the 3D volumes directly. The extant deep learning-based work is mainly focused on binary classification, but it is challenging to detect multiple stages with these methods. In this work, we propose a deep learning-based multiclass classification method to distinguish amongst various stages for the early diagnosis of Alzheimer’s. The proposed method significantly handles data shortage challenges by augmentation and manages to classify the 2D images obtained after the efficient pre-processing of the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our method achieves an accuracy of 98.9% with an F1 score of 96.3. Extensive experiments are performed, and overall results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of overall performance. Full article

It is an undeniable fact that people excessively rely on social media for effective communication. However, there is no appropriate barrier as to who becomes a part of the communication. Therefore, unknown people ruin the fundamental purpose of effective communication with irrelevant—and sometimes aggressive—messages. As its popularity increases, its impact on society also increases, from primarily being positive to negative. Cyber aggression is a negative impact; it is defined as the willful use of information technology to harm, threaten, slander, defame, or harass another person. With increasing volumes of cyber-aggressive messages, tweets, and retweets, there is a rising demand for automated filters to identify and remove these unwanted messages. However, most existing methods only consider NLP-based feature extractors, e.g., TF-IDF, Word2Vec, with a lack of consideration for emotional features, which makes these less effective for cyber aggression detection. In this work, we extracted eight novel emotional features and used a newly designed deep neural network with only three numbers of layers to identify aggressive statements. The proposed DNN model was tested on the Cyber-Troll dataset. The combination of word embedding and eight different emotional features were fed into the DNN for significant improvement in recognition while keeping the DNN design simple and computationally less demanding. When compared with the state-of-the-art models, our proposed model achieves an F1 score of 97%, surpassing the competitors by a significant margin. Full article

The potential of quantifying the variations in IR active bands was explored while using the chemometric analysis of FTIR spectra for selecting orthopedic biomaterial of industrial scale i.e., ultra-high molecular weight PE (UHMWPE). The nano composites UHMWPE with multi-walled carbon nano-tubes (MWCNTs) and Mg-silicate were prepared and irradiated with 25 kGy and 50 kGy of gamma dose. Principal component analysis (PCA) revealed that first three principal components (PCs) are responsible for explaining the >99% of variance in FTIR data of UHMWPE on addition of fillers and/or irradiation. The factor loadings plots revealed that PC-1 was responsible for explaining the variance in polyethylene characteristics bands and the IR active region induced by fillers i.e., 440 cm⁻¹, 456 cm⁻¹, from 900–1200 cm⁻¹, 1210 cm⁻¹, 1596 cm⁻¹, PC-2 was responsible for explaining the variance in spectra due to radiation-induced oxidation and cross linking, while the PC-3 is responsible for explaining the variance induced because of IR active bands of MWCNTs. Hierarchy cluster analysis (HCA) was employed to classify the samples into four clusters with respect to similarity in their IR active bands which is further confirmed by PCA. According to multi attribute analysis with PCA and HCA, 65 kGy irradiated sample is optimum choice from the existing alternatives in the group of irradiated pristine UHMWPE, UHMWPE/Mg-silicate irradiated with 25 kGy of gamma dose was the optimum choice for UHWMPE/Mg-silicate nano composites, and UHMWPE/γMWCNTs composites containing 1.0% dof γ MWCNTs for UHMWPE/MWCNTs nanocomposites, respectively. The results show the effectiveness of quantifying the variance for decision as far as optimization of biomaterials in orthopedic industrial applications is concerned. Full article