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Short-Term Forecasting for the Electricity Spot Prices With Extreme Values Treatment

IEEE ACCESS 2021年 9卷 105451-105462页

作者： Shah, Ismail Akbar, Sher Saba, Tanzila Ali, Sajid Rehman, Amjad Quaid I Azam Univ Dept Stat Islamabad 45320 Pakistan Prince Sultan Univ CCIS Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia

Nowadays, modeling and forecasting electricity spot prices are challenging due to their specific features, including multiple seasonalities, calendar effects, and extreme values (also known as jumps, spikes, or outliers). This study aims to provide a comprehensive analysis of electricity price forecasting by comparing several outlier filtering techniques followed by various modeling frameworks. To this end, extreme values are first treated with five different filtering techniques and are then replaced by four different outlier replacement approaches. Next, the spikes-free series is divided into deterministic and stochastic components. The deterministic component includes long-term trend, yearly and weekly seasonalities, and bank holidays and is estimated through parametric and nonparametric approaches. On the other hand, the stochastic component accounts for the short-run dynamics of the price time series and is modeled using different univariate and multivariate models. The one-day-ahead out-of-sample forecast results for the Italian Power Exchange (IPEX), obtained for a whole year, suggest that the outliers pre-filtering give a high accuracy gain. In addition, multivariate modeling for the stochastic component outperforms univariate models.

关键词： Predictive models Forecasting Time series analysis Analytical models data models Computational modeling Switches Electricity prices forecasting extreme values treatment IPEX parametric and nonparametric estimation

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Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

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MOBILE NETWORKS & APPLICATIONS 2024年第3期29卷 876-885页

作者： Rehman, Amjad Haseeb, Khalid Alruwaili, Fahad F. Ara, Anees Saba, Tanzila CCIS Prince Sultan Univ Artificial Intelligence & Data Analyt AIDA Lab Riyadh 12435 Saudi Arabia Shaqra Univ Coll Comp & Informat Technol Dept Comp & Network Engn Shaqra Saudi Arabia

The Internet of Things (IoT) and smart technologies perform a vital role in gathering data to facilitate extreme environments. These systems offer an autonomous management of real-time multimedia activities and are integrated with future networks. Smart communication and Cyber-Physical Systems (CPS) are tightly integrated with various processes to improve the efficiency, safety, and functionality of mobile networks. In recent decades, many approaches have been proposed to cope with the communication gap between physical objects and heterogeneous devices using adaptive routing techniques. Autonomous Multimedia CPS implementation using sensor technologies is significant for environmental monitoring, surveillance, and smart cities. However, reliable and fault-tolerant CPS communication in multimedia networks remains a research challenge. Most existing schemes deal with device mobility, which consumes energy and high latency communication in a constrained environment. In this research work, we proposed an optimized multimedia communication framework using adaptive intelligence for forwarding routes and decreasing the probability of data loss. It examines environmental parameters and circumstances for a prediction and decision-making system based on Particle Swarm Optimization (PSO). Furthermore, the proposed framework uses edge-enabled security to provide trusted sessions for devices with token-based authentication. Compared to existing work, the proposed framework demonstrates notable performance improvement for throughput, data latency, and alive nodes.

关键词： Cyber physical system Intelligent authentication Multimedia system Particle Swarm Optimization (PSO) Infrastructural development

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Insect classification and detection in field crops using modern machine learning techniques

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Information Processing in Agriculture 2021年第3期8卷 446-457页

作者： Thenmozhi Kasinathan Dakshayani Singaraju Srinivasulu Reddy Uyyala Machine Learning and Data Analytics Lab Centre of Excellence in Artificial IntelligenceDepartment of Computer ApplicationsNational Institute of TechnologyTiruchirappalliTiruchirappalli 620015Tamil NaduIndia

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious *** insect detection is a challenging task for farmers as a significant portion of the crops are damaged,and the quality is degraded due to the pest *** insect identification has the drawback of requiring well-trained tax-onomists to identify insects based on morphological features *** were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural net-works(ANN),support vector machine(SVM),k-nearest neighbors(KNN),naive bayes(NB)and convolutional neural network(CNN)*** paper presents the insect pest detec-tion algorithm that consists of foreground extraction and contour identification to detect the insects for Wang,Xie,Deng,and IP102 datasets in a highly complex *** 9-fold cross-validation was applied to improve the performance of the classification *** highest classification rate of 91.5%and 90%was achieved for nine and 24 class insects using the CNN *** detection performance was accomplished with less com-putation time for Wang,Xie,Deng,and IP102 datasets using insect pest detection *** comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy,computation time perfor-mance while apply more efficiently in field crops to recognize the *** results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

关键词： Crop pest classification Crop insect detection Image processing Machine learning Image segmentation

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Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator

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Computers, Materials & Continua 2022年第1期70卷 505-521页

作者： Summra Saleem M.Usman Ghani Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Saeed Ali Bahaj Department of Computer Science UETLahorePakistan Al-Khwarizmi Institute of Computer Science UETLahorePakistan Artificial Intelligence&Data Analytics Lab CCIS Prince Sultan University Riyadh11586Saudi Arabia MIS Department College of Business Administration Prince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia

Image translation plays a significant role in realistic image synthesis,entertainment tasks such as editing and colorization,and security including personal *** Edge GAN,the major contribution is attribute guided vector that enables high visual quality content *** research study proposes automatic face image realism from freehand sketches based on Edge *** propose a density variant image synthesis model,allowing the input sketch to encompass face features with minute *** density level is projected into non-latent space,having a linear controlled function *** assists the user to appropriately devise the variant densities of facial sketches and image *** data set of Large Scale CelebFaces Attributes(ClebA),labelled Faces in theWild(LFWH),Chinese University of Hong Kong(CHUK),and self-generated Asian images are used to evaluate the proposed *** solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.

关键词： Edge generator density variant sketch generator face translation recognition residual block

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Secure and energy-efficient framework using Internet of Medical Things for e-healthcare

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JOURNAL OF INFECTION AND PUBLIC HEALTH 2020年第10期13卷 1567-1575页

作者： Saba, Tanzila Haseeb, Khalid Ahmed, Imran Rehman, Amjad CCIS Prince Sultan Univ Riyadh Artificial Intelligence & Data Analyt Lab AIDA Riyadh 11586 Saudi Arabia Islamia Coll Peshawar Peshawar Pakistan Inst Management Sci Peshawar Pakistan

In various fields, the internet of things (IoT) gains a lot of popularity due to its autonomous sensors operations with the least cost. In medical and healthcare applications, the IoT devices develop an ecosystem to sense the medical conditions of the patients' such as blood pressure, oxygen level, heartbeat, temperature, etc. and take appropriate actions on an emergency basis. Using it, the healthcare-related data of patients is transmitted towards the remote users and medical centers for post-analysis. Different solutions have been proposed using Wireless Body Area Network (WBAN) to monitor the medical status of the patients based on low powered biosensor nodes, however, preventing increased energy consumption and communication costs are demanding and interesting problems. The issue of unbalanced energy consumption between biosensor nodes degrades the timely delivery of the patient's information to remote centers and gives a negative impact on the medical system. Moreover, the sensitive data of the patient is transmitting over the insecure Internet and prone to vulnerable security threats. Therefore, data privacy and integrity from malicious traffic are another challenging research issue for medical applications. This research article aims to a proposed secure and energy-efficient framework using Internet of Medical Things (IoMT) for e-healthcare (SEF-IoMT), which primary objective is to decrease the communication overhead and energy consumption between biosensors while transmitting the healthcare data on a convenient manner, and the other hand, it also secures the medical data of the patients against unauthentic and malicious nodes to improve the network privacy and integrity. The simulated results exhibit that the proposed framework improves the performance of medical systems for network throughput by 18%, packets loss rate by 44%, end-to-end delay by 26%, energy consumption by 29%, and link breaches by 48% than other states of the art solutions. (C) 2020 The Au

关键词： Internet of Medical Things (IoMT) Biosensors data security Energy efficiency Health system Healthcare

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Predicting mortality index for ICU inpatients based on clinical data extracted from electronic health record

Predicting mortality index for ICU inpatients based on clini...

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作者： Aisha Dawood Tanzila Saba Artificial Intelligence & Data Analytics Lab (AIDA) CCIS Prince Sultan University

Predicting ICU inpatients mortality index needs to be improved to incorporate clinical *** is also helpful to reflect the patient's recovery and hospitals *** this research machine learning model LightGBM was trained and *** study used a dataset for ICU admissions for adult patients from six *** a total of 130,000 patient records were included in the *** final model achieved AUROC(95% CI) of 0.97,an accuracy of 0.95,and an F1 score of 0.81 on the *** on results,it is observed that machine learning models with the support of conventional mortality scoring indices can provide a successful and useful model for predicting the outcome of critical and severe cases in the ICU.

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CrossDomain Recommendation Based on Metadata Using Graph Convolution Networks

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IEEE ACCESS 2023年 11卷 90724-90738页

作者： Khan, Rabia Iltaf, Naima Latif, Rabia Jamail, Nor Shahida Mohd Natl Univ Sci & Technol Dept Comp Software Engn Islamabad 46000 Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh 11586 Saudi Arabia

Recent advancements in the domain of recommender systems have stemmed from the inspiration of representing the user-item interaction into graphs. These heterogeneous graphs comprehensively capture the non-linear relationships between users and items alongwith features and emneddings. Graph Convolution Networks (GCNs) are state-of-the-art graph-based learning models that learn and represent the graph structures by recursively stacking layers of convolution and non-linear activation operations. GCNs are augmented with the strength of deep learning paradigms resulting in achieving better performance as compared to traditional CF (Collaborative Filtering) methods. Despite modern improvements in the domain of recommender systems, cold-start users are a daunting challenge in the design of recommender systems since the conventional recommendation services are based on solely one data source. During the recent years, cross-domain recommendation methods have gained popularity because of the availability of information in multiple domains for cold start users. We supplement this information by utilizing the data contained in the metadata of users alongwith the strength of modelling graphs using GCNs. Our proposed algorithm seams the strength of GCNs with cross-domain paradigm utilizing the richness in metadata in user's feedback to overcome the sparsity in user-item rating matrix. The combined advantages of GCNs and cross-domain approaches alleviated the issues of cold-start users by transferring user preferences from a source domain to a target domain.

关键词： Cross-domain recommendation collaborative filtering graph based representation deep learning coldstart users graph convolution networks

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Copy-move image forged information detection and localisation in digital images using deep convolutional network

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JOURNAL OF INFORMATION SCIENCE 2023年第5期49卷 1325-1334页

作者： Saba, Tanzila Rehman, Amjad Sadad, Tariq Mehmood, Zahid Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia Int Islamic Univ Dept CS & SE Islamabad Pakistan Univ Engn & Technol Dept Comp Engn Taxila Pakistan

Image tempering is one of the significant issues in the modern era. The use of powerful tools for image editing with advanced technology and its widespread on social media raised questions on data integrity. Currently, the protection of images is uncertain and a severe concern, mainly when it transfers over the Internet. Thus, it is essential to detect an anomaly in images through artificial intelligence techniques. The simple way of image forgery is called copy-move, where a part of an image is replicated in the same image to hide unwanted content of the image. However, image processing through handcrafted features usually looks for pattern concerns with duplicate content, limiting their employment for huge data classification. On the other side, deep learning approaches achieve promising results, but their performance depends on training data with fine-tuning of hyperparameters. Thus, we proposed a custom convolutional neural network (CNN) architecture with a pre-trained model ResNet101 through a transfer learning approach. For this purpose, both models are trained on five different datasets. In both cases, the impact of the model is evaluated through accuracy, precision, recall, F-score and achieved the highest 98.4% accuracy using the Coverage dataset.

关键词： Digital security fake information forgery detection ResNet101 transfer learning

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Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm optimization algorithm

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MICROSCOPY RESEARCH AND TECHNIQUE 2024年第5期87卷 1052-1062页

作者： Soleimani, Masoumeh Harooni, Aryan Erfani, Nasim Khan, Amjad Rehman Saba, Tanzila Bahaj, Saeed Ali Clemson Univ Dept Math & Stat Sci Clemson SC USA Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran Islamic Azad Univ Dept Comp Engn Dolatabad Branch Esfahan Iran Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Business Adm MIS Dept Al Kharj Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab CCIS Riyadh 11586 Saudi Arabia

The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. Research Highlights center dot To enhance the accuracy of cancer-type classifications based on microRNA expression data. center dot We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.

关键词： cancer health risks microRNA particle swarm optimization radial basis function neural network

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LSTMDD: an optimized LSTM-based drift detector for concept drift in dynamic cloud computing

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PEERJ COMPUTER SCIENCE 2024年 10卷 e1827页

作者： Mehmood, Tajwar Latif, Seemab Jamail, Nor Shahida Mohd Malik, Asad Latif, Rabia Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci SEECS Islamabad Pakistan Prince Sultan Univ Coll Comp & Informat Sci CCIS Artificial Intelligence & Data Analyt Lab Riyadh Saudi Arabia

This study aims to investigate the problem of concept drift in cloud computing and emphasizes the importance of early detection for enabling optimum resource utilization and offering an effective solution. The analysis includes synthetic and realworld cloud datasets, stressing the need for appropriate drift detectors tailored to the cloud domain. A modified version of Long Short-Term Memory (LSTM) called the LSTM Drift Detector (LSTMDD) is proposed and compared with other top drift detection techniques using prediction error as the primary evaluation metric. LSTMDD is optimized to improve performance in detecting anomalies in nonGaussian distributed cloud environments. The experiments show that LSTMDD outperforms other methods for gradual and sudden drift in the cloud domain. The findings suggest that machine learning techniques such as LSTMDD could be a promising approach to addressing the problem of concept drift in cloud computing, leading to more efficient resource allocation and improved performance.

关键词： Concept drift Drift detection Cloud usage trace CPU usage Memory usage Machine learning

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