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Öğe Abalone age prediction using machine learning(Springer Science and Business Media Deutschland GmbH, 2022) Guney, Seda; Kilinc, Irem; Hameed, Alaa Ali; Jamil, AkhtarAbalone is a marine snail found in the cold coastal regions. Age is a vital characteristic that is used to determine its worth. Currently, the only viable solution to determine the age of abalone is through very detailed steps in a laboratory. This paper exploits various machine learning models for determining its age. A comprehensive analysis of various machine learning algorithms for abalone age prediction is performed which include, backpropagation feed-forward neural network (BPFFNN), K-Nearest Neighbors (KNN), Naive Bayes, Decision Tree, Random Forest, Gauss Naive Bayes, and Support Vector Machine (SVM). In addition, five different optimizers were also tested with BPFFNN to evaluate their effect on its performance. Comprehensive experiments were performed using our data set. © 2022, Springer Nature Switzerland AG.Öğe Advanced Generative AI Methods for Academic Text Summarization(Institute of Electrical and Electronics Engineers Inc., 2024) Dar, Zaema; Raheel, Muhammad; Bokhari, Usman; Jamil, Akhtar; Alazawi, Esraa Mohammed; Hameed, Alaa AliThe exponential growth of scientific literature emphasizes the need for employing advanced techniques for effective text summarization, which can significantly speed up the research process. This study tackles the challenge by advancing scientific text summarization through AI and deep learning methods. We delve into the integration and fine-tuning of cutting-edge models, including LED-Large, Pegasus variants, and BART, aiming to refine the summarization process. Unique combinations, such as SciBERT with LED-Large, were investigated to ensure the capture of critical details frequently missed by traditional methods. This novel approach led to notable improvements in summarization effectiveness. Our findings indicate that models like LED-Large excel in quickly adapting to training data, achieving impressive semantic understanding with fewer training epochs, evidenced by achieving a FRES score of 28.5852 and ROUGE scores, including a ROUGE-l F1-Score of 0.4991. However, while extensively trained models like BART -large and Pegasus displayed strong semantic capabilities, they also pointed to the necessity for refinements in readability and higher-order n-gram overlap in the produced summaries. © 2024 IEEE.Öğe An optimized feature selection approach using sand Cat Swarm optimization for hyperspectral image classification(Elsevier, 2024) Hameed, Alaa Ali; Jamil, Akhtar; Seyyedabbasi, AmirIntegrating metaheuristic algorithms and optimization techniques with remote sensing technology has accelerated the advent of advanced methodologies for analyzing hyperspectral images (HSIs). These images, rich in detail across a broad spectral range, are pivotal for diverse applications. However, the high dimensionality of data poses challenges for obtaining optimal results therefore, a preprocessing step is necessary to reduce the dimensionality of the data to select the most effective features before the application of machine learning models. This study introduces a novel methodology that integrates Back Propagation (BP) and Variable Adaptive Momentum (BPVAM) with Sand Cat Swarm Optimization (SCSO) for the classification of hyperspectral images. Utilizing SCSO for the optimal feature selection followed by BPVAM generated more accurate classification maps. The fusion of the unique strengths of SCSO with the flexibility of BPVAM has significantly boosted the precision, efficiency, and adaptability of HSI classification. The effectiveness of our method is demonstrated using two benchmark hyperspectral datasets and validated through a comprehensive comparison with other benchmark optimization techniques, including Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Our findings indicate that our approach enhances classification accuracy that is comparable to the stateof-the-art methods in the domain of hyperspectral data analysis.Öğe Applications and Associated Challenges in Deployment of Software Defined Networking (SDN)(Springer Science and Business Media Deutschland GmbH, 2023) Baniya, Pashupati; Agrawal, Atul; Nand, Parma; Bhushan, Bharat; Hameed, Alaa Ali; Jamil, AkhtarSDN, a rising technology within the realm of Internet of Things (IoT), has been increasingly well-received in recent times. This article presents a summary of SDN along with its different elements, advantages, and difficulties. The paper aims to provide practical solutions for introducing OpenFlow into commercial routers without hardware modifications and extending the integration of OpenFlow with legacy control protocols and control planes. In addition, the paper presents a refactoring process for migrating traditional network applications to OpenFlow-based ones, focusing on the security challenges and techniques of open technologies like SDN, OpenROADM, and SDN-based Mobile Networks (SDMN). The document also examines the advantages and possible uses of SDMN in enhancing network adaptability, streamlining network administration, and bolstering network security. The article also discusses O-RAN network innovations and difficulties, such as AI and ML workflows that are made possible by the architecture and interfaces, security concerns, and, most importantly, standardization issues. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Artificial intelligence approach for modeling house price prediction(Institute of Electrical and Electronics Engineers Inc., 2022) Çekiç, Melihşah; Korkmaz, Kübra Nur; Mukus, Habib; Hameed, Alaa Ali; Jamil, Akhtar; Soleimani, FaezehIndexed keywords SciVal Topics Abstract Real estate has a vast market volume across the globe. This domain has been growing significantly in the past few decades. An accurate prediction can help buyers, and other decision-makers make better decisions. However, developing a model that can effectively predict house prices in complex environments is still a challenging task. This paper proposes machine learning models for the accurate prediction of real estate house prices. Furthermore, we investigated the feature importance and various data analysis methods to improve the prediction accuracy. Linear Regression, Decision Tree, XGBoost, Extra Trees, and Random Forest were used in this study. For all models, hyperparameters were first calculated using k-fold cross-validation, and then they were trained to apply to test data. The models were tested on the Boston housing dataset. The proposed method was evaluated using Root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) metrics.Öğe Assessing the spreading behavior of the Covid-19 epidemic: a case study of Turkey(Institute of Electrical and Electronics Engineers Inc., 2022) Demir, Erdem; Canıtez, Muhammed Nafiz; Elazab, Mohamed; Hameed, Alaa Ali; Jamil, Akhtar; Al-Dulaimi, Abdullah AhmedCoronavirus (Covid-19) disease is a rapidly spreading type of virus that was discovered in Wuhan, China, and emerged towards the end of 2019. During this period, various studies were conducted, and intensive studies are continued in different fields regarding coronavirus, especially in the field of medicine. The virus continues to spread and is yet to be controlled fully. Machine learning is a well-explored field in the domain of computer science that can learn patterns based on existing data and make predictions on new data. This study focused on using various machine learning approaches for predicting the spreading behavior of the COVID-19 virus. The models that were considered include SARIMAX, Extreme Gradient Boosting (XGBoost), Linear Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Artificial Neural Network (ANN). The models were trained and then predictions were made by applying these models to the daily updated data provided by the Turkish Ministry of Health. Experiments on the test data showed that both XGBoost and Decision Tree models outperformed other models.Öğe CryptStego: Powerful Blend of Cryptography and Steganography for Securing Communications(Springer Science and Business Media Deutschland GmbH, 2024) Pandey, Shraiyash; Baniya, Pashupati; Nand, Parma; Hameed, Alaa Ali; Bhushan, Bharat; Jamil, AkhtarIn today’s era, security is one of the most critical issues in the development of electronic communications applications, especially when sending private data. The data may be encrypted with several algorithms; however, an extra layer of security can improve protection by a significant amount. Therefore, in this paper, we have developed an application, CryptStego, to secure data using two techniques, cryptography and steganography, to transmit data securely. The encryption of original data is executed using Blowfish algorithm, a cryptographic technique. Additionally, the encrypted data is hidden through using Least Significant Bit (LSB), a steganography technique. The implementation of both techniques offers an extensive level of security, since an intruder must firstly identify the encrypted text within the image to attain the encrypted text, then secondly to decrypt using the algorithm to obtain the original message. Therefore, any intruder must encounter multiple levels of security to obtain the original message from the cipher image. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Cyber Threat Analysis and Mitigation in Emerging Information Technology (IT) Trends(Springer Science and Business Media Deutschland GmbH, 2024) Imam, Mohsin; Wajid, Mohd Anas; Bhushan, Bharat; Hameed, Alaa Ali; Jamil, AkhtarFor the information technology sector, cybersecurity is essential. One of the main issues in the modern world is sending information from one system to another without letting the information out. Online crimes, which are on the rise daily, are the first thing that comes to mind when we think about cyber security. Various governments and businesses are adopting a number of actions to stop these cybercrimes. A lot of individuals are still quite worried about cyber security after taking many safeguards. This study’s primary goal is to examine the difficulties that modern technology-based cyber security faces, especially in light of the rising acceptance of cutting-edge innovations like server less computing, blockchain, and artificial intelligence (AI). The aim of this paper is to give readers a good overview of the most recent cyber security trends, ethics, and strategies. This study focuses on the present state of cyber security and the steps that may be taken to address the rising dangers posed by modern technology through a thorough investigation of the existing literature and actual case studies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Öğe Deep learning for liver disease prediction(Springer Science and Business Media Deutschland GmbH, 2022) Mutlu, Ebru Nur; Devim, Ayse; Hameed, Alaa Ali; Jamil, AkhtarMining meaningful information from huge medical datasets is a key aspect of automated disease diagnosis. In recent years, liver disease has emerged as one of the commonly occurring diseases across the world. In this paper, a Convolutional Neural Network (CNN) based model is proposed for the identification of liver disease. Furthermore, the performance of CNN was also compared with traditional machine learning approaches, which include Naive Bayes (NB), Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Logistic Regression (LR). For evaluation, two datasets were used: BUPA and ILPD. The experimental results showed that CNN was effective for the classification of liver disease, which produced an accuracy of 75.55%, and 72.00% on the BUPA and ILPD datasets, respectively. © 2022, Springer Nature Switzerland AG.Öğe Deep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce(Institute of Electrical and Electronics Engineers Inc., 2024) Aamir, Fatima; Sherafgan, Raheimeen; Arbab, Tehreem; Jamil, Akhtar; Close Bhatti, Fazeel Nadeem; Hameed, Alaa AliSearching is the process of information retrieval utilizing specific criteria or keywords. Integrating search function-alities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models de-signed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strate-gies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries Dataset and the Upstart Commerce catalog datasets. © 2024 IEEE.Öğe Efficient artificial intelligence-based models for COVID-19 disease detection and diagnosis from CT-Scans(Institute of Electrical and Electronics Engineers Inc., 2022) Masood, Muhammad Zargham; Jamil, Akhtar; Hameed, Alaa AliCOVID-19 is contagious virus that first emerged in China in 2019's last month. It mainly infects the both the lungs and the respiratory system. The virus has severely impacted life and the economy, which exposed threats to governments worldwide to manage it. Early diagnosis of COVID-19 could help with treatment planning and disease prevention strategies. In this study, we use CT-Scanned images of the lungs to show how COVID-19 may be identified using transfer learning model and investigate which model achieved the best and fastest results. Our primary focus was to detect structural anomalies to distinguish among COVID-19 positive, negative, and normal cases with deep learning methods. Every model received training with and without transfer learning and results were compared for various versions of DenseNet and EfficientNet. Optimal results were obtained using DenseNet201 (99.75%). When transfer learning was applied, all models produced almost similar results.Öğe Enhancing robotic manipulator fault detection with advanced machine learning techniques(Iop Publishing Ltd, 2024) Khan, Faiq Ahmad; Jamil, Akhtar; Khan, Shaiq Ahmad; Hameed, Alaa AliThe optimization of rotating machinery processes is crucial for enhanced industrial productivity. Automatic machine health monitoring systems play a vital role in ensuring smooth operations. This study introduces a novel approach for fault diagnosis in robotic manipulators through motor sound analysis to enhance industrial efficiency and prevent machinery downtime. A unique dataset is generated using a custom robotic manipulator to examine the effectiveness of both deep learning and traditional machine learning in identifying motor anomalies. The investigation includes a two-stage analysis, initially leveraging 2D spectrogram features with neural network architectures, followed by an evaluation of 1D MFCC features using various conventional machine learning algorithms. The results reveal that the proposed custom CNN and 1D-CNN models significantly surpass traditional methods, achieving an F1-score exceeding 92%, highlighting the potential of sound analysis for automated fault detection in robotic systems. Additional experiments were carried out to investigate 1D MFCC features with various machine learning algorithms, including KNN, DT, LR, RF, SVM, MLP, and 1D-CNN. Augmented with additional data collected from the locally designed manipulator, our experimental setup significantly enhances model performance. Particularly, the 1D-CNN stands out as the top-performing model on the augmented dataset.Öğe Exploring Deep Learning-based Approaches for Brain Tumor Diagnosis from MRI Images(Institute of Electrical and Electronics Engineers Inc., 2024) Abdullah, Fasih; Jamil, Akhtar; Alazawi, Esraa Mohammed; Hameed, Alaa AliBrain tumors significantly impair health due to their aggressive growth, necessitating rapid and accurate detection and classification for effective treatment. Traditional methods relying on medical professionals' manual MRI analysis are time-consuming and resource-intensive. This study leverages Artificial Intelligence-based deep learning approaches to streamline and enhance the accuracy of brain tumor classification from MRI images. We evaluated five advanced deep learning models: a custom CNN, DeepTumor-Net, VGG-16, ResNet-50, and Xception. These models were applied to the 'Brain tumors 256×256' dataset, classifying brain tumors into four distinct categories: no tumor, Glioma, Meningioma, and Pituitary Tumors. These models were trained and then fine-tuned, which further refined their performance. The models were evaluated using the standard evaluation metrics, including accuracy, precision, recall, F1-Score, specificity, and Cohen's Kappa. The final results showed that high accuracies can be obtained for MRI classification using these deep learning models. Notably, ResNet-50 and VGG-16 stood out with test accuracies of 92.6 % and 92.1 %, respectively, indicating their significant potential in medical imaging analysis. © 2024 IEEE.Öğe A faster dynamic convergency approach for self-organizing maps(SPRINGER HEIDELBERG, 2022) Jamil, Akhtar; Hameed, Alaa Ali; Orman, ZeynepThis paper proposes a novel variable learning rate to address two main challenges of the conventional Self-Organizing Maps (SOM) termed VLRSOM: high accuracy with fast convergence and low topological error. We empirically showed that the proposed method exhibits faster convergence behavior. It is also more robust in topology preservation as it maintains an optimal topology until the end of the maximum iterations. Since the learning rate adaption and the misadjustment parameter depends on the calculated error, the VLRSOM will avoid the undesired results by exploiting the error response during the weight updation. Then the learning rate is updated adaptively after the random initialization at the beginning of the training process. Experimental results show that it eliminates the tradeoff between the rate of convergence and accuracy and maintains the data's topological relationship. Extensive experiments were conducted on different types of datasets to evaluate the performance of the proposed method. First, we experimented with synthetic data and handwritten digits. For each data set, two experiments with a different number of iterations (200 and 500) were performed to test the stability of the network. The proposed method was further evaluated using four benchmark data sets. These datasets include Balance, Wisconsin Breast, Dermatology, and Ionosphere. In addition, a comprehensive comparative analysis was performed between the proposed method and three other SOM techniques: conventional SOM, parameter-less self-organizing map (PLSOM2), and RA-SOM in terms of accuracy, quantization error (QE), and topology error (TE). The results indicated the proposed approach produced superior results to the other three methods.Öğe Improving the performance of self-organizing map using reweighted zero-attracting method(Elsevier B.V., 2024) Hameed, Alaa Ali; Jamil, Akhtar; Alazzawi, Esraa Mohammed; Marquez, Fausto Pedro Garcia; Fitriyani, Norma Latif; Gu, Yeonghyeon; Syafrudin, MuhammadIn this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM. © 2024 The AuthorsÖğe Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI(Institute of Electrical and Electronics Engineers Inc., 2024) Khan, Shaiq Ahmad; Ahmad Khan, Faiq; Jamil, Akhtar; Hameed, Alaa AliIn industries, machines communicate through sounds, decoded by predictive maintenance to prevent issues. Understanding motor sounds is crucial for seamless industrial operations. This research undertakes a comprehensive explo-ration of machine learning models, specifically Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, applied to motor sound data for classifying instances as either healthy or faulty. The ANN, boasting an 81.22 % accuracy, reveals commendable precision and recall values for both classes, indicating its robust predictive capabilities. However, there is room for improvement, particu-larly in accurately classifying healthy motors. SVM marginally outperforms the ANN with an accuracy of 81.32%, showcasing balanced precision and recall for both classes. Notably, KNN, while exhibiting a slightly lower accuracy of 80.22 %, excels in recall for the healthy class, emphasizing its efficacy in correctly identifying healthy motor sounds. Random Forest attains an accuracy of 81.32 %, featuring notably high recall for the healthy class (0.91), underscoring its proficiency in capturing instances of healthy motor sounds. In-depth metrics provide nuanced insights into the strengths and specificities of each model, offering a foundation for informed decisions based on application priorities and requirements. The study contributes not only quantitative metrics but also interpretability tools, including LIME and SHAP, to enhance transparency and elucidate the intricate patterns within motor sound data. © 2024 IEEE.Öğe Machine Learning Based Techniques for Failure Detection and Prediction in Unmanned Aerial Vehicle(Institute of Electrical and Electronics Engineers Inc., 2024) Mustafa, Ata; Jamil, Akhtar; Hameed, Alaa AliUnmanned aerial vehicles (UAVs) are aircraft with-out human pilot on the board. UAVs have two flight mode: Auto and Manual. In Auto mode, UAV follows a predefined path. The path is embedded in the control system of UAV. In manual mode, human operator in ground control station controls the trajectory of the vehicle remotely. UAVs have diverse applications in military as well as civil sectors. UAVs have to operate in a variety of unseen environments. The diverse usage and uncertainty in operational environment demand safe and reliable operation. Timely identification and rectification of faults stand as a crucial requirement for the operation. One of the major cause of UAV failure is engine fault. In this paper we investigate affectiveness of machine learning techniques regarding engine fault detection and prediction. We analyzed the techniques on AirLab Failure and Anomaly (ALFA) Dataset. For fault detection we used Multi-Layer Perceptron, Random Forest, Support Vector Machine, Ada Boost, Gradient Boosting, Logistic Regression and Single Dimensional Convolutional Neural Network. We observed that Random Forest is most effective technique for fault detection with F-l Score of 0.99. Regarding fault prediction we tried LSTM and GRU based network in different settings. Gated Recurrent Unit performed best with F -1 Score of 0.99 while predicting fault four second ahead of time. © 2024 IEEE.Öğe Monocular vision with deep neural networks for autonomous mobile robots navigation(Elsevier, 2022) Sleaman, Walead Kaled; Hameed, Alaa Ali; Jamil, AkhtarEnabling mobile robots to explore the formerly unidentified environment is a challenging task. The current paper describes the internal analysis algorithm for mobile robots that combines various convolutional neural network (CNN) layers with the decision-making process in a hierarchical way. The whole system is trained end-to-end on data captured by a low-cost depth camera (RGB-D). The output consists of the proposed expansion model of the robot's critical moving directions to achieve autonomous analysis ability. Training this model through the dataset is created using Hand-Controlled Mobile Robot (HCMR) built for this purpose. The experiments were conducted by moving this robot in natural and diverse environments. The robot was trained using this data and applied for environmental investigation decisions (the control labels) using CNN to enable the robot to automatically sense the navigation without a map in an unknown environment. Furthermore, extensive experiments were conducted indoors and attained an accuracy of 77%. Experiments showed that the proposed model was able to reach equivalent results that are generally obtained enormously from an expensive sensor. In addition, comprehensive comparisons were drawn between the human-controlled robot and a robot trained using a deep learning process to determine decisions to control the robot's movement. The reached results were identical and satisfactory.Öğe Special issue on computing, intelligence and data analytics for wisdom (CIDA4Wisdom)(Wiley, 2024) Eken, Suleyman; Solak, Serdar; Bilgehan Ucar, M. Hikmet; Kilimci, Zeynep Hilal; Jamil, Akhtar; Hameed, Alaa Ali; Garcia Marquez, Fausto Pedro[Abstract Not Available]Öğe Understanding the user-generated geographic information by utilizing big data analytics for health care(HINDAWI LTD, 2022) Ullah, Hidayat; Hameed, Alaa Ali; Rizvi, Sanam Shahla; Jamil, Akhtar; Kwon, Se JinThere are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants. Keywords
