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

作者： Fizzah Sohail Hira Haider Najma Ismat Software Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan Computer Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan

The rise in Internet of Things (IoT) devices with inadequate resources resulted in the emergence of the fog-computing, an extension of the cloud computing paradigm. In cloud computing, the network edge contains all fog cells that cause a reduction in the latency. It enables the implementation of IoT applications on the resources of computational Fog. IoT implementation aims to optimize the need for quality service (QoS) while maximizing fog resource utilization. Fog computing also enables a new generation of services and applications by extending the Cloud Computing paradigm to the network edge. Fog is defined with the help of the characteristics such as Mobility, location awareness and low latency, a significant number of nodes, major wireless access role, extensive geographical, prevalent real-time applications, and streaming. In this study, comprehensive taxonomy is proposed, a comparison between cloud and fog computing has been highlighted, the key features of Fog Computing, also Fog-based applications is presented in detail. Moreover, it is argued that Fog is a suitable platform for various IoT applications. These characteristics include Actuators Networks, Wireless sensors, smart cities, smart Grid, and connected vehicles. At last, the paper elaborates the potential trends of Fog Computing.

关键词： Wireless communication Cloud computing Wireless sensor networks Privacy Computational modeling Market research Internet of Things

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Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

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Informatica (Slovenia) 2021年第7期45卷 147-166页

作者： Jamil, Danish Palaniappan, Sellappan Lokman, Asiah Naseem, Muhammad Zia, syed Saood Department of Information Technology Malaysia University of Science and Technology Kuala Lumpur Malaysia Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan

Many researchers are trying hard to minimize the incidence of cancers, mainly GC. For GC, the five-year survival rate is generally 5-25%, but for EGC, it is almost up to 90%. Among the cancers, GC is very deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The healthcare sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need further improvement in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is essential to investigate this area further and develop more optimized approaches for early diagnosis. Early detection will increase the chances of successful treatments. In this study, we have conducted a literature survey detailing the role of AI in the healthcare sector for GC diagnosis. We discuss basic principles, advantages and disadvantages, training and testing of data, and integration of applications like DSS, CDSS, KDD, ML, DM, BD, and DL, and their relevance to the healthcare industry. The study focuses on the application of ML techniques used in the diagnosis of GC. This review paper also introduces DM techniques, their application in the healthcare industry, limitations, roles, and operational challenges. These assist pathologists in helping minimize their workload while increasing diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Decision support systems

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Optimizing Underwater Acoustic Sensor Networks: Insights from Energy-Efficient, Reliable, and Void Issue-Based Routing Protocols

Optimizing Underwater Acoustic Sensor Networks: Insights fro...

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

作者： Muhammad Umar Khan Muhammad Aamir Waleej Haider Department of Telecommunication Engineering Sir Syed University of Engineering and Technology Pakistan Department of Computer Science and Information Technology Sir Syed University of Engineering and Technology Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

The Underwater Acoustic Sensor Network (UASN) is based on the sensory elements that detect the environmental conditions of submerged regions and transmit the information through acoustic waves at the sink node placed on top of the water. All these sensory elements are battery-operated just like the Terrestrial Wireless Sensor Network (TWSN). However, these nodes are not stationary, unlike the nodes in TWSN. Thus, routing with limited energy and ever-changing topology decreases the reliability of the network and increases the chances of Void existence. This research paper focuses on the comparative analysis by critically reviewing some of the state-of-the-art routing protocols which are based on the effective usability of the limited energy resource, reliability of the network, and Void occurrence issues. An analytical method is employed to examine the strengths and shortcomings of existing routing protocols, with a specific emphasis on the identified difficulties. It provides a solid foundation related to UASN routing protocols which are primarily designed with a focus on resolving issues such as limited energy, low reliability, and high chances of void occurrence. The contribution of this comparative study helps the research partners from the academy and industry which are new to this field of study and would like to contribute.

关键词： Wireless communication Wireless sensor networks Energy resolution Solids Routing protocols Energy efficiency Topology Reliability Underwater acoustics Usability

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Cache Algorithms for software Defined Network Efficiency: An Analytical Approach

Cache Algorithms for Software Defined Network Efficiency: An...

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

作者： Warda Haider Shehnila Zardari Shiza Riaz Memon Muhammad Aamir Department of Software Engineering NED University of Engineering & Technology Karachi Pakistan Department of Telecommunication Engineering Sir Syed University of Engineering & Technology Karachi Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

In today's networking environments caching algorithms plays a very important role to get the best use of resources and maximizing performance of networks. In this paper, we conduct a comparative analysis of the three caching algorithms: Dependent set, Cover set and Mixed set. Every algorithm is intended to solve one problem of rule dependency management and cache optimization in the network switches. Each has its strengths and trade-offs, and a detailed comparison across dozens of dimensions, throughput, adaptability, memory usage, cache-hit rate, execution time, resource efficiency, scalability (both operational- and system-level), flexibility in supporting different workloads, robustness to system behavior changes independent of user behavior (like the slow generation asymptote), and operational stability just to name a few popular ones, are relevant for many high-scale web services products. There are many other dependencies in the region, so pay a lot of memory which is not acceptable for the very first versions. The Dependent Set algorithm is designed to be an optimized memory, easy to adapt and resource-efficient rule dependency manager. This further enforces the emphasis on rule coverage policies of the Cover Set algorithm, improving cache performance and scalability. The Mixed Set algorithm combines the strengths of both methods providing a rounded approach, with throughput, adaptability and resource allocation. The study examines the benefits of each algorithm in real-world situations. It shows how they improve network efficiency and adaptability. This research expands our understanding of caching algorithms. It explains how they optimize networks. This paves the way for better network management and performance. By comparing these algorithms, the paper provides useful information. It helps network administrators and researchers enhance caching mechanisms in networks.

关键词： Wireless communication Web services Scalability software algorithms Throughput Stability analysis Robustness Resource management software defined networking Thermal stability

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Enhancing Contextualized GNNs for Multimodal Emotion Recognition: Improving Accuracy and Robustness

Enhancing Contextualized GNNs for Multimodal Emotion Recogni...

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International Multi-Topic ICT Conference (IMTIC)

作者： Muhammad Huzaifa Shahbaz Zain-Ul-Abidin Khalid Mahboob Fayyaz Ali Software Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan

Emotion recognition from facial expressions is an important research area in the field of artificial intelligence. In this study, a novel deep-learning model is proposed for emotion recognition from facial expressions. The model uses a combination of modifications in COGMEN (Contextualized GNN-based Multimodal Emotion Recognition) to extract features from facial images and other modalities and capture temporal dependencies between frames. The model was evaluated on the IEMOCAP dataset, achieving an accuracy of 87.8%, outperforming (mostly) existing state-of-the-art methods. Furthermore, the model has a lower computational complexity compared to other methods, making it more practical for real-time applications. A high F1 score was reported for each emotion category, indicating good performance across all classes. The results demonstrate the potential method for improving emotion recognition from multiple modalities (i.e.: Audio, Visual & Text) and have implications for a wide range of applications such as affective computing, human-computer interaction, and mental health monitoring.

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Expression Detection Of Autistic Children Using CNN Algorithm

Expression Detection Of Autistic Children Using CNN Algorith...

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

作者： Abeer Javed syed Deenar Jawaid Durrani Neha Shahid Wajahat Khan Ali Muhammad Software Engineering Department Sir Syed University Of Engineering & Technology Karachi Pakitan

Facial emotion detection is a technique for identifying human emotions from facial expressions. Autism Spectrum Disorder is an advanced neurobehavioral disorder, autism is a vast field with many problems, including hyperactivity, sensory, speech, etc. Children with autism who have behavioral problems are the focus of this research. This research is on an automatic detection system that will detect the expressions of autistic children and generate a report explaining their behaviors during their therapy sessions. This automatic system is primarily focusing on three main behavioral issues: happy, neutral, and irritated. This research is done to provide facilities to the parents, therapists, and Autistic Rehabilitation Centers to keep records of their children’s and patients’ progress in the form of reports. This is carried out using machine learning and image processing methods. The autistic children's faces are used to extract the features using local binary patterns. The proposed system uses CNN, Haar Cascade object detection Algorithm, and TensorFlow. The classification of emotions and face detection is done using the CNN algorithm and Haar Cascade object detection algorithm. Helping Hands Rehabilitation Center, a Pakistan-based autism rehabilitation center has provided its utmost assistance to make this research possible.

关键词： Wireless communication Autism Pediatrics Wireless sensor networks Machine learning algorithms Object detection Feature extraction

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Cartesian Product Based Transfer Learning Implementation for Brain Tumor Classification

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Computers, Materials & Continua 2022年第11期73卷 4369-4392页

作者： Irfan Ahmed Usmani Muhammad Tahir Qadri Razia Zia Asif Aziz Farheen Saeed Electronic Engineering Department Sir Syed University of Engineering&TechnologyKarachi75300Pakistan Department of Computer Science Bahria University Karachi CampusKarachi75260Pakistan Christus Trinity Clinic TexasUSA

Knowledge-based transfer learning techniques have shown good performance for brain tumor classification,especially with small ***,to obtain an optimized model for targeted brain tumor classification,it is challenging to select a pre-trained deep learning(DL)model,optimal values of hyperparameters,and optimization algorithm(solver).This paper first presents a brief review of recent literature related to brain tumor ***,a robust framework for implementing the transfer learning technique is *** the proposed framework,a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters:batch size and learning *** extensive exercise consisting of 435 simulations for 11 state-of-the-art pre-trained DL models was performed using 16 paired hyperparameters from the Cartesian product matrix to input the model with the three most popular solvers(stochastic gradient descent with momentum(SGDM),adaptive moment estimation(ADAM),and root mean squared propagation(RMSProp)).The 16 pairs were formed using individual hyperparameter values taken from literature,which generally addressed only one hyperparameter for optimization,rather than making a grid for a particular *** proposed framework was assessed using a multi-class publicly available dataset consisting of glioma,meningioma,and pituitary *** assessment shows that ResNet18 outperforms all other models in terms of accuracy,precision,specificity,and recall(sensitivity).The results are also compared with existing state-of-the-art research work that used the same *** comparison was mainly based on performance metric“accuracy”with support of three other parameters“precision,”“recall,”and“specificity.”The comparison shows that the transfer learning technique,implemented through our proposed framework for brain tumor classification,outperformed all existing *** the best of our knowledge,the proposed framework is a

关键词： Deep transfer learning Cartesian product hyperparameter optimization magnetic resonance imaging(MRI) brain tumor classification

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Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer 6

Implementation of Convolutional Neural Networks deep learnin...

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

作者： Afroz, Adnan Zia, Razia Noor, Shaheena Garcia, Andres Ortiz Shams, Sarman Mughal, Shoaib Sir Syed University Engineering and Technology Software Enginneering Department Karachi Pakistan Universidad de Malaga E.T.S Ingenieria de Telecomunicacion Malaga Spain Bahria University Computer Engineering Department Campus Karachi Pakistan

ISBN: (纸本)9798350333718

A kind of deadly skin tumor called melanoma is arguably very lethal since it accounts for the majority of skin cancer fatalities. Melanomas are often brown or black in color because melanocyte cells, which produce melanin, are the source of the disease. The DNA of skin cells is damaged by UV light, which is the main cause of melanomas. The analysis of the dermoscopy examination's findings and comparison with medical sciences are frequently used in the manual diagnosis of melanoma cancer. Human subjectivity has a strong impact on manual detection, which renders it unreliable in some circumstances. In order to classify the outcomes of the dermoscopy test and to determine the results more precisely with a comparatively shorter amount of time, computer-Assisted technology is required. Problem statement, planning, execution, and testing are the first steps in the creation of this application. To identify picture data, the research study combines deep machine learning approach using Convolutional Neural Network technique along with LeNet-5 architectural model. With a variable number of training and testing epochs and 44 photos from the training results, the experiment with the best success rate (93% in training and 100% in testing) required 176 images and 100 epochs of training data. Programming language such as Python with utilization of Keras library, which serves as the Tensorflow back-end, were used to construct this application. © 2023 IEEE.

关键词： Python

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IoT-Based Soil and Weather Sensors for Crop Monitoring: A Step towards Smart Agriculture 7

IoT-Based Soil and Weather Sensors for Crop Monitoring: A St...

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

作者： Qamar, Nadia Ali, Mir Shabbar Siddique, Abdul Mohimeen Qureshi, Haris Uddin Ali, Uzair Khurram, Ayesha A. Sir Syed University of Engineering & Technology Civil Engineering Department Karachi Pakistan Limited Pakistan Karachi Pakistan Ned University Crop2x Pvt.Ltd National Incubation Center Pakistan

ISBN: (纸本)9798331534271

For a sustainable agricultural production, a thorough understanding of the interrelationship between the crop yield and soil properties, weather conditions, farming practice, and freshwater availability is essential. The conventional methods for monitoring the daily soil and weather conditions in a crop field have been found to be time-consuming and laborious, where a limited real-time field information leads to soil deterioration, excessive or deficient irrigation, and ultimately reduced crop water productivity and crop yield. Therefore, this study was conducted to investigate the effectiveness of IoT-based smart soil sensors and weather trackers for monitoring the soil and weather conditions as an alternative to the conventional methods. The initial crop growing phase was taken as the study period as 21st November 2022-10th December 2022. The results obtained for November showed the average daily soil moisure, soil temperature,, soil TDS, relative humidity, air temperature, and heat Index as 18.24 %, 17.7 °C, 200 ppm, 8126 %, 17.4 °C, and 21.6 °Crespectively, whilefor December, theaveragedaily soil moisture, soil temperature soil TDS, relative humidity, air temperature, and heat Indexwasfoundas 17.7%, 15.1 °C, 167 ppm, 75.8%, 14.53 °C, and 24.3 °C respectively. Thus, the IoT-based smart soil sensors and weather trackers can be adopted in place of the conventional methods for the real-time monitoring of soil and weather conditions that may assist the farm managers for a timely irrigation scheduling, better on-farm water management, soil conservation, and to achieve the desired crop water productivity and the per hectare crop yield. © 2024 IEEE.

关键词： Smart agriculture

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

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

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

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

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

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.

关键词： Wireless communication Symbiosis Reviews Tissue engineering Proteomics Propulsion Repeaters Prediction algorithms Biological materials Diseases

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