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An Algorithm to Reduce Compression Ratio in Multimedia Applications

computers, Materials & Continua 2023年第1期74卷 539-557页

作者： Dur-e-Jabeen Tahmina Khan Rumaisa Iftikhar Ali Akbar Siddique Samiya Asghar Department of Electronic Engineering Sir Syed University of Engineering&TechnologyKarachiPakistan Department of Information&Computer Science Sir Syed University of Engineering&TechnologyKarachiPakistan

In recent years,it has been evident that internet is the most effective means of transmitting information in the form of documents,photographs,or videos around the *** purpose of an image compression method is to encode a picture with fewer bits while retaining the decompressed image’s visual *** transmission,this massive data necessitates a lot of channel *** order to overcome this problem,an effective visual compression approach is required to resize this large amount of *** work is based on lossy image compression and is offered for static color *** quantization procedure determines the compressed data quality *** images are converted from RGB to International Commission on Illumination CIE La^(∗)b^(∗);and YCbCr color spaces before being *** the transform domain,the color planes are encoded using the proposed quantization *** improve the efficiency and quality of the compressed image,the standard quantization matrix is updated with the respective image *** used seven discrete orthogonal transforms,including five variations of the Complex Hadamard Transform,Discrete Fourier Transform and Discrete Cosine Transform,as well as thresholding,quantization,de-quantization and inverse discrete orthogonal transforms with CIE La^(∗)b^(∗);and YCbCr to RGB *** to signal noise ratio,signal to noise ratio,picture similarity index and compression ratio are all used to assess the quality of compressed *** the relevant transforms,the image size and bits per pixel are also *** the(n,n)block of transform,adaptive scanning is used to acquire the best feasible compression *** of these characteristics,multimedia systems and services have a wide range of possible applications.

关键词： Color image compression color spaces discrete orthogonal transforms(DOTs) peak-to-signal noise ratio(PSNR) similarity index

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A systematic review of machine learning and deep learning techniques for anomaly detection in data mining

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International Journal of computers and Applications 2025年第2期47卷 169-187页

作者： Tahir, Mahjabeen Abdullah, Azizol Izura Udzir, Nur Azhar Kasmiran, Khairul Serdang Malaysia Serdang Malaysia Department of Computer Engineering Sir Syed University of Engineering & Technology Karachi Pakistan

The growing use of the internet has increased the threat of cyberattacks. Anomaly detection systems are vital for protecting networks by spotting irregular activities. Various studies investigated anomaly detection techniques without a systematic approach. So far, the existing reviews mainly concerned time series and data streaming methods almost neglected the growing interest in graph-based data mining techniques which are vital in social networks, finance, and IoT domains. Following PRISMA guidelines, this systematic review examines anomaly detection methods applied to time series, data streaming, and graph-based data from 2018 to 2023. A total of 34 papers were selected from four key databases ScienceDirect, Scopus, Web of Science, and IEEE. In addition, this review addressed several issues with existing techniques including low scalability, explainability, and interpretability for real-time anomaly detection systems. In modern applications where data structures are complex, and processing requirements are high these traditional techniques are insufficient for real-time data processing. Finally, our study demanded more advanced, complex methods to address these evolving challenges. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Data streams

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Forecasting karachi's Air Temperature Variation: Leveraging Mobile and Multimedia Dataset for Global Warming Insights

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Journal of Mobile Multimedia 2024年第6期20卷 1289-1320页

作者： Zaffar, Asma Ahmed, Shakil Rafique, Aysha Imran, Muhammad Amir, Muhammad Uddin, Vali Department of Mathematics and Sciences Sir Syed University of Engineering and Technology Karachi Pakistan Department of Computer Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Department of Telecommunication Engineering Sir Syed University of Engineering and Technology Pakistan Department of Civil Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Sir Syed University of Engineering and Technology Professor and Dean Computer and Electrical Engineering Karachi Pakistan Sir Syed University of Engineering and Technology Professor and Vice Chancellor Karachi Pakistan

In karachi, the transport and building industries have a major impact on carbon dioxide (CO2) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time ... 详细信息

In karachi, the transport and building industries have a major impact on carbon dioxide (CO₂) emissions and greenhouse gas emissions. This study is innovative in that it examines how air temperature changes over time and forecasts the maximum and minimum temperatures in karachi. Specifically, it looks at 60 years' worth of mean monthly maximum and minimum air temperatures in karachi, spanning from (1961 to 2020) dataset classes. The monthly average air temperature in karachi, both at its minimum and maximum, remains constant. Using dataset from the Pakistan Metrological department (PMD) and, the ARMA (p, q) model technique was used to assess forecasting and modelling the behavior of the maximum and minimum air temperatures in karachi. The least values Akaike information criterion (AIC), the Bayesian Schwarz information criterion (SIC), and the Hannan Quinn information criterion (HIC) are used to describe how adequate the model is. Additionally, the Durbin-Watson (DW) test is used. The average monthly maximum and minimum air temperature in karachi are strongly correlated, as indicated by DWvalues (<2). The lowest and maximum monthly average air temperatures in karachi are predicted using diagnostic checking techniques such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Theil's U-Statistics. The results indicate that there is a strong correlation between the air temperature and previously measured values, with Theil's U-Statistics values for each month lying close to zero. This study is a great resource for observing how air temperature affects global warming. This study also highlights the the innovative aspect of utilizing vast datasets, possibly including those from mobile and multimedia sources, to address a critical environmental issue. It also emphasizes the predictive modelling aspect of your study, which is central to understanding and mitigating global warming effects. © 2025 River Publisher

关键词： air temperature ARMA (p kurtosis q) model root mean square error skewness

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

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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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Converting 2D Images into 3D Holograms: An Analysis of Modern Holographic technology 7

Converting 2D Images into 3D Holograms: An Analysis of Moder...

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

作者： Khalil, Nida Nawaz, Mohammad Talha Ejaz, Mahnoor Zahid, Shanzay Ahmed, Aryan Sir Syed University of Engineering & Technology Department of Software Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

Several cutting-edge modern technologies, including hologram technology, have emerged due to the tremendous advancements of our era. The science of holography is used to make holograms, which are 3D images with lifelike graphics. With its vibrant 3D views and immersive experiences, holographic technology transforms into a different form of fields like education, entertainment, advertising, etc. This study outlines an easier way to modify the 2D images into 3D holograms. Blender software and a holographic fan projector are used in this process. The process begins with Blender, the 3D graphics suite, is opened and converts flat images into more detailed animated models. These are then sent to a mobile app which transfers them onto a holographic display easily. The mobile application acts as a convenient medium for users to customize or manage their pictures. Utilizing innovative holographic technology to produce lifelike 3D images, the inquiry focuses on obtaining images from the mobile app and projecting them onto the holographic display. The human hologram and building structure are the two areas covered in this study. In effect, this article essentially assists construction businesses with building structures, and educational institutions employ human holograms for lecture purposes. This research also investigates how the advantages of holographic models compare to other fast-growing platforms both now and soon. © 2024 IEEE.

关键词： Holograms

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