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Models Prediction and Estimation of ENSO and Karachi Rainfall Cycles Through AR-GARCH and GARCH Process

Journal of Mobile Multimedia 2024年第6期20卷 1251-1288页

作者： Zaffar, Asma Khan, Rizwan Malik, Nimra Amir, Muhammad Uddin, Vali Asif, Muhammad Department of Mathematics and Sciences Sir Syed University of Engineering and Technology Karachi Pakistan Business Development Manager at National Center of GIS and Space Applications (NCGSA) Institute of Space Technology (IST) Islamabad Pakistan Sir Syed University of Engineering and Technology Karachi Pakistan Sir Syed University of Engineering and Technology Karachi Pakistan Sir Syed University of Engineering and Technology Karachi Pakistan

Karachi rainfall and ENSO cycles have influenced on earth climates. The effects of El-Nino Southern Oscillation (ENSO) on the rainfall climate system of the Karachi region are analyzed. The research is working on the ENSO and rainfall Karachi region massive datasets information gathered for the period 1961-2021, which are break down into cycles (1st - 10th). The novelty of this study is to analyze the factor of the ENSO effect, which is parallel to the Karachi rainfall region, as well as other factors such as deforestation. ENSO-Rainfall Karachi region cycles are also measured via statistical techniques. The estimate Model and forecasting of volatility through the comparative study of AR(R) - GARCH (P, Q) and GARCH (P, Q) Models. Effect of El Nĩno-Southern Oscillation (ENSO) on Karachi Rainfall as a case study suitable to research on its behavior for estimating forecast evolution of ENSO-Rainfall Karachi region cycles. The technique of AR(R) - GARCH (P, Q) and GARCH (P, Q) process is feasible for ensuring the appropriateness of the impacts on Karachi region rainfall and ENSO cycle. Different value of AR(R) - GARCH (P, Q) and GARCH (P, Q) Models is used. RMSE, MAE, MAPE and U Test are calculated to describe which technique provide the maximum accuracy of forecasting and Predictions. Most of the forecasting evaluation describe that GARCH (P, Q) has highest accuracy to predicting and forecasting ENSO and Karachi Rainfall Cycles as comparative others three models. This study confirms that, the duration of ENSO years, the tendency of Karachi region rainfall is reduced. The study shows that the relationship between ENSO-Rainfall Karachi region cycles are uncertainty. In the immediate year following the El Nino event, the July and September months, as well as the summer monsoon season, had a statistically significant 90% rainfall deficit. © 2025 River Publishers.

关键词： ENSO ENSO Forecasting GARCH (P Karachi region Q) RMSE

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Review on Image Processing for MRI-Focused Brain Tumor Identification Using Machine Learning 7

Review on Image Processing for MRI-Focused Brain Tumor Ident...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Agha, Dur-E-Shawar Qazi, Farheen Rasheed, Rohail Ali Khan, M. Sadiq Naseem, Muhammad Mubeen, Tauseef Sir Syed University of Engineering & Technology Software Engineering Department Karachi Pakistan Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Today, image processing plays a significant role in early disease detection, allowing doctors to treat conditions like breast, lung, and brain malignancies. A brain tumor is a serious condition that can be fatal. Brain tumor diagnosis is the most important task for early therapy, and automatic visual classification might be extremely important. It is essential to classify brain cancers in order to assess the tumor's and determine the best course of treatment for each class. To find brain tumors, numerous imaging modalities are used. However, MRI is frequently utilized since it produces superior images and uses no ionizing radiation. This research aims to provide a review of different Machine Learning (ML) techniques for detection and classification of brain tumor. Also, different feature extraction techniques are analyzed and their benefits are discussed. This review is conducted by different research papers which helps new researchers to start immediately a literature review and knowing different machine learning techniques their advantages and disadvantages to classify brain tumor through MRI. Also, they know the accuracy of each algorithm which leads them to directly target the problem not implementing the existing techniques. © 2024 IEEE.

关键词： Lung cancer

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AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

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Computers, Materials & Continua 2024年第2期78卷 2419-2440页

作者： Moona Kanwal Najeed AKhan Aftab A.Khan Computer Science and Information Technology Department NED University of Engineering and TechnologyKarachiPakistan Computer Engineering Department Sir Syed University of Engineering and TechnologyKarachiPakistan College of Education Health and Human ServicesLongwood UniversityFarmvilleVAUSA

The user’s intent to seek online information has been an active area of research in user *** profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and *** user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content *** user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social ***,a combination of multiple behaviors in profiling users has yet to be *** research takes a novel approach and explores user intent types based on multidimensional online behavior in information *** research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine *** research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data *** feedback is based on online behavior and practices collected by using a survey *** participants include both males and females from different occupation sectors and different *** data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their *** techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of *** average is computed to identify user intent type *** user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on th

关键词： User intent cluster user profile online search information sharing user behavior search reasons

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Comparative Techniques Using Hierarchical Modelling and Machine Learning for Procedure Recognition in Smart Hospitals

Journal of ICT Standardization

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Journal of ICT Standardization 2022年第2期10卷 145-164页

作者： Noor, Shaheena Aamir, Muhammad Ismat, Najma Saleem, Muhammad Imran Department of Computer Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Department of Telecommunication Engineering Sir Syed University of Engineering & Technology Karachi Pakistan

6G is one of the key cornerstone elements of the futuristic smart system setup – the others being cloud computing, big data, wearable devices and Artificial Intelligence. Also, smart offices and homes have become even more popular than before, because of the advancement in computer vision and Machine Learning (ML) technologies. Recognition of human actions and situations are fundamental components of such systems, especially in complex environments like healthcare, for example at the dentist clinic, where we need cues such as eye movement to distinguish procedures being undertaken. In this work, we compare models based on hierarchical modelling and machine learning to identify the dental procedure. We used the objects seen while following the eye trajectories and focussed on elements including material used for treatment, equipment involved and the teeth conditions i.e. symptoms. Our experiments showed that using Artificial Neural Network (ANN) increased the accuracy of prediction compared to hierarchical modelling. Our experiments show an improvement in accuracy for each of the constituent parameters i.e., symptom (ANN: 95.58% vs. Hierarchical: 45.68%), material (ANN: 86.32% vs. Hierarchical: 45.18%) and equipment (ANN: 92.65% vs. Hierarchical: 59.39%). © 2022 River Publishers

关键词： Machine learning

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Smart Biomaterials and AI-Driven Approaches for the Tissue engineering Advancement 7

Smart Biomaterials and AI-Driven Approaches for the Tissue E...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ahmed, Rubaisha Waqar syed, Sidra Abid Raziq, Mariam Sir Syed University of Engineering and Technology Biomedical Engineering Department Karachi Pakistan

ISBN: (纸本)9798331534271

The field of the tissue engineering has witnessed remarkable-progress in recent years, largely propelled by the convergence of the smart biomaterials and AI-driven methodologies. Biomaterials with their great ability to interface with the biological systems but also have revolutionized tissue engineering by providing the scaffolds that mimics the native extra-cellular matrix. Concurrently, the well-known AI-artificial intelligence has become a formidable tool for the data analysis, prediction, and an optimization. Assimilating the vast data-sets from the genomics, proteomics, and the clinical studies, AI-driven algorithms guide biomaterial-designs patient specific treatments strategies, and the diseases modeling. This review paper systematically examines recent studies and developments where the synergy of the smart biomaterials and AI-driven approaches have contributed to the tissue engineering advancements. Moreover, this study-paper highlights how AI-algorithms deciphers the complex biological data-sets, offering insights into disease mechanisms and aiding in the customization of biomaterials-based interventions. This as-well under-scores their collective potentials to re-define the landscape of tissue engineering, creating personalized treatments, accelerating drug discovery, and ultimately improving the patients outcomes. © 2024 IEEE.

关键词： Tissue regeneration

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An Automated Model for Fabric Weave Pattern Recognition and Classification Using Deep Learning Based Image Processing 7

An Automated Model for Fabric Weave Pattern Recognition and ...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Pasha, Quratulain Akram, Noreen Rafique, Ayesha Akber, Anam Abbasi, Madiha Sir Syed University of Engineering & Technology Telecommunication Engg. Department Karachi Pakistan Sir Syed University of Engineering & Technology Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331534271

Fabric weave type identification has many uses that adds to importance of advance classification methods;e.g fabric weave should be known in order to identify the most suitable use. But the manual inspection of fabric causes human errors such as, stressed, less focused, eye focus and mishandling of the apparatus. There are lighting effects which also degrades the performance of manual inspection. In recent years, computer vision based approaches have achieved remarkable performance in the fields of object recognition and image processing, thus opening a new door to develop an automated system for the recognition of woven fabric patterns for production of fine quality fabric according to customer needs. In this paper, we have devised an automatic weave pattern recognition mechanism based on convolutional neural networks(CNN). Initially three basic weave patterns were chosen i.e Twill, Plain and Satin. The results were analysed for its possible adaptability in the task of fabric weave pattern recognition. The classification results give the average accuracy of 72% for all three samples whereas individually, Plain weave gives 100% accuracy, Twill gives 66% accuracy and satin weave gives 50% only, which indicates a room for improvement in future. © 2024 IEEE.

关键词： Convolutional neural networks

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A Scalable and Generalized Deep Ensemble Model for Road Anomaly Detection in Surveillance Videos

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Computers, Materials & Continua 2024年第12期81卷 3707-3729页

作者： Sarfaraz Natha Fareed A.Jokhio Mehwish Laghari Mohammad siraj Saif A.Alsaif Usman Ashraf Asghar Ali Department of Information Technology Quaid-e-Awam University of EngineeringScience&TechnologyNawabshah67480Pakistan Department of Software Engineering Sir Syed University of Engineering and TechnologyKarachi75000Pakistan Electrical Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia School of Business Torrens UniversitySydneyNSW 2007Australia School of Engineering and Information Technology The University of New SouthWales(UNSD)CanberraACT 2604Australia

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security *** Circuits Television(CCTV)Cameras are used to surveillance and monitor the normal and anomalous ***-world anomaly detection is a significant challenge due to its complex and diverse *** is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems,and the need for automated techniques has been raised to enhance detection *** paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection(SERAD).SERAD is used to detect and classify the four most happening road anomalies,such as accidents,car fires,fighting,and snatching,through road surveillance videos with high *** SERAD adapted three pre-trained Convolutional Neural Networks(CNNs)models,namely VGG19,ResNet50 and *** stacking technique is employed to incorporate these three models,resulting in much-improved accuracy for classifying road abnormalities compared to individual ***,it presented a custom real-world Road Anomaly Dataset(RAD)comprising a comprehensive collection of road images and *** experimental results demonstrate the strength and reliability of the proposed SERAD model,achieving an impressive classification accuracy of 98.7%.The results indicate that the proposed SERAD model outperforms than the individual CNN base models.

关键词： Convolutional neural network transfer learning stack ensemble learning road anomaly detection data augmentation

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A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction 7

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Jamil, Danish Shah, syed Mehr Ali Al-Jarwani, Fatma Mahfoodh Mohammed Sir Syed University of Engineering & Technology Department of Software Engineering Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331534271

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care. © 2024 IEEE.

关键词： Lung cancer

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Analyzing the Classification Performance of DenseNet121 on Pre-processsed MIAS Dataset 6

Analyzing the Classification Performance of DenseNet121 on P...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Mubeen, Tauseef Zain-Ul-Abidin Shahbaz, Muhammad Huzaifa Roth, Pablo Otero Nieto, Miguel Angel Luque Sir Syed University of Engineering and Technology Software Engineering Department Karachi Pakistan University of Malaga Department of Telecommunication Engineering Malaga Spain

ISBN: (纸本)9798350333718

Breast cancer is the most diagnosed type of cancer as per the data that is collected by the World Health Organization (WHO) within the past few years. Over 600,000 deaths were recorded in 2021 due to breast cancer. Breast cancer screening is done using two-dimensional (2D) and three-dimensional (3D) mammography, but MRIs and Ultrasounds are also used in certain conditions. The diagnosis from the screenings is not always accurate as a practitioner must physically look at the digital images to find any signs of cancer. Approximately, each diagnosis has a variable chance of a false-positive or a false-negative. Many CAD (computer-Aided detection) systems have been developed for the assistance of a practitioner with the diagnosis. However, in the past years, Deep Neural Networks (DNN) have seen a spike and the models are being used to aid breast cancer screening. Data shows a possibility of reaching Area under the curve (AUC) values as high as 0.99 under ideal conditions when the training dataset is cleaned of noise and properly pre-processed and in some studies, the accuracy and sensitivity are even compared to that of a practitioner's, with the DNN model outperforming in numbers across the board. After performing a literature review on similar work, we have trained a model of our own on a publicly available dataset (MIAS) reaching promising results of an AUC of 0.87 and an Accuracy of 0.88 with the initial model built on a DenseNet121 architecture. © 2023 IEEE.

关键词： Deep neural networks

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Improved Skin Lesion Classification Using Gaussian Filtering and Mobile Net-Based Convolutional Neural Networks 7

Improved Skin Lesion Classification Using Gaussian Filtering...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Khan, Sadiq Ali Raza, Asif Anwar, Maleeha Siddiqui, Osama Ahmed University of Karachi Department of Computer Sciences Karachi Pakistan Sir Syed University of Engineering & Technology Department of Computer Science & It Karachi Pakistan Dawood University of Engineering & Technology Karachi Pakistan Sir Syed University of Engineering & Technology Department of Computer Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

Every year, thousands of people throughout the world are diagnosed with skin cancer and enrolled for treatment. Cancer of the skin is one of the forms of cancer that is responsible for the deaths of millions of people every year. The early diagnosis and treatment of newly diagnosed instances of severe skin cancer are very necessary in order to guarantee a high survival rate in addition to a low mortality rate. The custom neural network architecture that has been proposed makes use of a custom CNN for feature extraction and integrates Mobile-Net as its foundation model. For diagnosis, the CNN layers have nine separate classifications. Training and testing the algorithm, the very large ISIC Data Set, which can be accessed via Kaggle, is used. This collection of data contains a large number of images that illustrate the many phases of skin cancer. Based on the findings of this research, the validation and training loss are both rather low, reaching 0.15 and 0.14 respectively. To locate the precise positions of cancer lesions, a CNN-based feature extraction method is used along with a Mobile-Net pre-trained model. Adam optimizer has been implemented with a learning rate of 0.001%, which enables the algorithm to become more effective and learn more efficiently. The accuracy of Mobile-Net models is encouraging enough with a 99.06% training accuracy and a 98.11% of validation accuracy. For further information, this model demonstrates a considerable increase in terms of outcomes for patients and survival rate. © 2024 IEEE.

关键词： Lung cancer

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