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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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KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring 4

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Sikander, Saad Rehman, Hamood Ur Naqvi, syed Fakhr Abbas Aslam, Waqar Perwaiz, Nazia Sir Syed CASE Institute of Technology Department of Electrical Engineering Islamabad Pakistan Sir Syed CASE Institute of Technology Department of Electrical Engineering and Computer Science Islamabad Pakistan National University of Sciences and Technology NUST School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures - specifically, Ki-UNet and Attention UNet - and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://***/SaadBaloch96/Dataset/blob/main/***) © 2024 IEEE.

关键词： Environmental monitoring

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks 7

A Comparative Study of the Performance of Real time database...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, syed Muhammd Nabeel Farooq, Muhammad Umer Zehra, syeda Sundus Noronha, Jospeh Peter Terenece Ned University of Engineering and Technology Dept. of Computer Science & I.T Karachi Pakistan Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics. © 2023 IEEE.

关键词： Big data

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NAVTRAK: Differential GPS-Based Location Tracking and Long-Range Multi-Hop Communication of Marine Moving Objects

NAVTRAK: Differential GPS-Based Location Tracking and Long-R...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Shahid, Ammar Lodhi, Noor Elahi Khan Khan, Muhammad Farooq Maaz, Maaz Rukaiya, Rukaiya Jugnoo, Farooq Ahmed Sir Syed University of Engg. and Tech. Computer Engineering Dept. Karachi Pakistan -MTC Dept. Karachi Pakistan

ISBN: (纸本)9798350349719

The paper presents a real-Time location tracking and long-range multi-hop communication system using (Global Positioning System) GPS-enabled LoRa devices. The system uses differential corrections in the GPS data received at the communicating marine stations. Differential corrections are calculated at the Base Station and applied to the moving stations' GPS measurements to increase the location precision. Furthermore, the system provides multi-hop routing based on RSSI and the hop count of the LoRa nodes. NAVTRAK performs real-Time tracking and monitoring of moving ships and sends them alert messages in critical situations. The experimental results show that the system improves the accuracy of GPS measurements by up to 1 meter. The designed system is not restricted to being used for marine applications and is feasible in scenarios (e.g., navigation, surveying, and other scientific applications) where accurate positioning is critical, the equipment cost and infrastructure need to be justified by the benefits of increased accuracy and communication range. © 2024 IEEE.

关键词： Global positioning system

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Comparative Evaluation of Acoustic Channel Characteristics for Reliable Design of Underwater Acoustic Sensor Networks (UASN) 6

Comparative Evaluation of Acoustic Channel Characteristics f...

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

作者： Khan, Muhammad Umar Aamir, Muhammad Shams, Rehan Otero, Pablo Jilani, Umair Saleem, Faizan Sir Syed University of Engineering & Technology Telecommunication Engineering Department Karachi Pakistan Universidad de Malaga Department of Telecommunication Sir Málaga Spain

ISBN: (纸本)9798350333718

Due to unexplored but desirable undersea resources, Underwater Sensor Network, is developing technology and becomes a catchword for modern scientists. Underwater Acoustic Sensor Networks, also known as UASN, are employed for a broad range of underwater utilizations such as reconnaissance by the military, mineral ore detection, oil reservoir management, aquatic environment exploration, imaging marine life, and early warnings to prevent a natural disaster. Almost every application is triggering to monitor important characteristics such as the salinity concerning different levels of depth, temperature profile, pollutants, etc. However, the study of aquatic channel characteristics is required before implementing the UASN for any of the applications. This research paper presents the underwater channel characteristics by varying the acoustic signal frequency, underwater depth levels, and separation between nodes. The simulated result is generated using MATLAB. The findings in the form of graphical representations are satisfactory and may be considered for future research in UASN. © 2023 IEEE.

关键词： Underwater acoustics

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COVID-19 Classification from X-Ray Images:An Approach to Implement Federated Learning on Decentralized Dataset

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computers, Materials & Continua 2023年第5期75卷 3883-3901页

作者： Ali Akbar Siddique S.M.Umar Talha M.Aamir Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Department of Telecommunication Engineering Sir Syed University of Engineering&TechnologyKarachi75300Pakistan Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its *** to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big *** who test positive for Covid-19 are diagnosed via a nasal PCR *** comparison,polymerase chain reaction(PCR)findings take a few hours to a few *** PCR test is expensive,although the government may bear expenses in certain ***,subsets of the population resist invasive testing like ***,chest X-rays or computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response *** advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest *** article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning *** order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the *** two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the *** proposed model is trained using an image dataset that can be found on *** are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are *** edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has *** is no association between the datasets of the various nodes that are included in the *** doing it in this manner,each of the nodes will have access to a separate image collection that has no correl

关键词： Artificial intelligence deep learning federated learning COVID-19 decentralized image dataset

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Acoustic modelling using deep learning for Quran recitation assistance

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International Journal of Speech technology 2023年第1期26卷 113-121页

作者： Ghori, Ammar Farid Waheed, Aisha Waqas, Maria Mehmood, Aqsa Ali, syed Abbas Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Recent advancements in computer science and engineering have significantly boosted interest in and success rate of speech recognition systems. Sophisticated speech recognition systems are being developed for a variety of languages having wide-spread applications. Arabic being one of the most widely used languages in the world, augmented by religious importance, is a potential candidate in this domain, however rich vocabulary and multiple dialects open up new challenges for development of Arabic speech recognition processes. This paper focuses on development of an Arabic isolated speech recognition system for the vocabulary of Holy Quran using Mel frequency cepstral coefficients and deep neural networks. The proposed system is capable of identifying individual words from a recited verse with reasonable accuracy. It targets the 362 unique words of the first and the last 19 chapters of the Holy Quran and uses 14 hours of audio data to demonstrate the working prototype of the system. A user-friendly, web-based application has also been developed for transcription of recitations. The software is specified to make an effort in spreading the right way of recitation of the Holy Quran. The presented architecture can be extended to a complete automatic speech recognition system to help people practice recitations, mapping speech to transcription and then matching the transcribed utterance with standard templates of correct recitation to identify reciter’s short comings. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep neural networks

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Outlook 360 | A Practical Approach of Recruiting Talent Through Artificial Intelligence. 3

Outlook 360 | A Practical Approach of Recruiting Talent Thro...

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3rd International Conference on Innovations in computer Science and Software engineering, ICONICS 2022

作者： Zehra, Sani E Shakil, Areeba Fatima, Maryam Munawwar, M. Anas Khalil, Nida Sir Syed University of Engineering and Technology Software Engineering Department Karachi Pakistan

ISBN: (纸本)9781665461412

Intellectual capital acquired the Position of an asset in the fourth industrial revolution. An asset that would help companies to reach their targeted tier in the 'talent war' and help them maintain their bottom lines for significant setbacks. With the unforeseen consequences of Covid-19, this asset has become one benefit that needs to be retained. It implies that staying aware of the rapidly developing techniques for recruitment and implementing talented or skilled individual is one factor that assists companies in winning the talent war: The conversation objective, a potential employee to invest significant time and energy in a digitalized world. A better-performing e-recruitment application can bring employees to apply for the job. Thus, this paper sheds light on what upgrades and progressions are necessary for AI-based e-recruitment systems to draw and attain skilled individuals and help organizations achieve their target tier out in the corporate world. © 2022 IEEE.

关键词： Artificial intelligence

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

Improved Skin Lesion Classification Using Gaussian Filtering...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

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

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

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.

关键词： Training Wireless communication Accuracy Neural networks Feature extraction Skin Classification algorithms Convolutional neural networks Lesions Skin cancer

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Development of an Algorithm based on the Optimization of space, time and resources of the warehouse using Genetic Algorithms 3

Development of an Algorithm based on the Optimization of spa...

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3rd International Conference on Innovations in computer Science and Software engineering, ICONICS 2022

作者： Babar, M. Mubashir syed, Abeer Javed Asim, Shaarif Khan, Abdul Rafay Hai, M. Abdul Sir Syed University of Engineering and Technology Department of Software Engineering Karachi Pakistan

ISBN: (纸本)9781665461412

The warehouse is often the starting point in a supply chain. Ineffectiveness at a warehouse can cascade into the rest of the supply chain, hurting the customers' experience with the business in the long run. Warehouse Optimization System (WOS) play a crucial role in resolving a significant market issue by optimizing warehouse space and logistical procedures, documenting merchant supply and demand patterns, and making it simpler to handle goods that are about to expire, are out of stock, are broken, or have been abandoned by customers. This issue relates to space and storage in the warehouse, due to the fact that business intelligence, which may include algorithms for anticipating sales and managing warehouse capacity, may be connected to warehouse optimization systems. A case study is described in research of a warehouse optimization system, followed by a demonstration of the problem of gaps within the racks of warehouses. The algorithm solves the problem of fitting boxes with varying dimensions into racks. By using automation and careful design, warehouse optimization increases the effectiveness of how time, space, and resources are used in a warehouse, which enhances customer satisfaction. © 2022 IEEE.

关键词： Genetic algorithms

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