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Predicting Lung Cancer Risk Using Artificial Intelligence

Predicting Lung Cancer Risk Using Artificial Intelligence

引用

Computing and Data Analytics (ICCDA), International Conference on

作者： syed Mehr Ali Shah Danish Jamil Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed Al-Jarwani College of Computing & Information Sciences University of Technology and Applied Sciences shinas Sultanat of Oman Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research proposes a method to accurately forecast the risk of lung cancer in its initial stages by the combination of medical diagnosis and artificial intelligence. Two distinct models are employed: a symptoms-based model utilizing linear regression, and VGG19, a component of CNNs is employed for the analysis of chest x-ray images. The objective of this dual-model approach is to improve comprehensiveness and precision in the early prediction of lung cancer risk. The JSRT and Chest Xray-14 datasets were utilized to train and validate the convolutional neural network (CNN). The Regression model was trained using the Survey Lung cancer Dataset. This initiative prioritizes user-friendliness, interpretability, and ethical considerations of healthcare AI. Initial positive results show that this combined approach has a lot of potential for early detection

关键词： Surveys Analytical models Ethics Accuracy Linear regression Lung cancer Convolutional neural networks Medical diagnosis Artificial intelligence X-ray imaging

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Identification of Damaged Date Palm Tree in a Farm using IoT-based Thermal Image Analysis

Identification of Damaged Date Palm Tree in a Farm using IoT...

引用

2023 IEEE International Conference on Computer, Information, and Telecommunication Systems, CITS 2023

作者： Nadeem, Adnan Ashraf, Muhammad Mehmood, Amir Siddiqui, Muhammad Shoaib Almoamari, Hani Abbasi, Qammer H. Islamic University of Madinah Faculty of Computer and Information Systems Madinah42351 Saudi Arabia Federal Urdu University of Arts Science and Technology Department of Physics Karachi75300 Pakistan Sir Syed University of Engineering and Technology Department of Computer Science and Information Technology Karachi75300 Pakistan James Watt School of Engineering University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9798350336092

The Kingdom of Saudi Arabia is one of the world's leading fresh date producers. Identifying damaged date palm trees on the farm is important for saving water and stopping the spread of pest which has caused the damage. In this paper, we aim to evaluate the performance of IoT-based thermal image camera input for the automatic detection of damaged trees in a Date Tree farm. The thermal images will be obtained by an IoTbased thermal camera attached to UAV. Then the temperature histogram vector will be generated which will be fed to the SVM for identification of the damaged tree. The system will raise the alarm along with the coordinates of the damaged tree for the farmer to proactively deal with the issue. The initial results of the tested scenario are encouraging for its detailed implementation in the future. © 2023 IEEE.

关键词： Cameras

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis

Empowering Precision Machine: Deep Learning Technique for Lu...

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Computing and Data Analytics (ICCDA), International Conference on

作者： Danish Jamil syed Mehr Ali Shah Muhammad Numan Ali Khan Fatma Mahfoodh Mohammed AlJarwani Galician Studies and Development Institute (IDEGA) University of Santiago de Compostela Spain Department of Software Engineering Sir Syed University of Engineering & Technology College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Sultanat of Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics.

关键词： Deep learning Technological innovation Computed tomography Computational modeling Precision medicine Lungs Lung cancer Medical services Data models Medical diagnostic imaging

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Improving Traffic Surveillance: Deep Learning Approach for Road Anomaly Detection in Videos

Improving Traffic Surveillance: Deep Learning Approach for R...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Sarfaraz Natha Muhammad Arif syed Shahryar Jamil Fareed Ahmed Jokhio Muslim Jameel syed Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Instituto Politécnico Nacional (IPN) Mexico MAJU Karachi Pakistan Department of Computer Systems Engineering Quaid-e-Awam University of Engineering and Technology Nawabshah Pakistan Department of Enterprise and Technology Atlantic Technological University Galway Ireland

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. This study introduces an automatic detection framework for recognizing road anomalies such as accidents, fighting, car fires, and armed snatching (gunpoint) in road surveillance videos. After reviewing the literature, the review directs that convolutional neural networks (CNNs) are a specialized deep learning approach well suited for image and video analysis. The proposed methodology combines the pretrained CNN models with Data Augmentation (DA) techniques to fine-tune hyperparameters such as learning rate and momentum that enhance the model learning accuracy and performance for recognizing road anomalies. Furthermore, it introduced a rolling prediction algorithm to solve the flickering problem during testing and created a new road anomaly dataset (RAD) as a benchmark consisting of road surveillance videos and images. Our proposed model combined with the InceptionV3 pre-trained model achieved a best accuracy is $98.81\%$ for detection and classification as compared to other deep learning models.

关键词： Deep learning Accuracy Roads Surveillance Computational modeling Prediction algorithms Data augmentation

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Technological, Financial and Ecological Analysis of Photovoltaic Power System using RETScreen®: A Case in Khuzdar, Pakistan

Technological, Financial and Ecological Analysis of Photovol...

引用

2022 International Conference on Emerging Technologies in Electronics, Computing and Communication, ICETECC 2022

作者： Alvi, Um-E-Habiba Ahmed, Ijaz Alvi, Alveena Ashfaq, Babar Mukhtar, Sana Ali, Paghunda Roheela Pieas Department of Electrical Engineering Islamabad Pakistan University of Aveiro Department of Management and Economics Aveiro Portugal Giki Department of Mechanical Engineering Topi Pakistan Sir Syed Case Institute of Technology Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9781665490870

Green energy projects can benefit greatly from using RETScreen as a preliminary estimate for financial analysis and technology assessment. Energy shortages and climate change are wreaking havoc in Pakistan at the present time. In this study, we examine green energy sources to meet the country's energy needs while keeping environmental concerns in mind. The NASA climate information is used to conduct a comprehensive technical, economical, and environmental analysis of the intended green plant. In addition, the statistical indications demonstrate that the project is feasible in terms of cost-emission-savings. It is anticipated that this strategy will make it easier to start new renewable energy projects in developing countries that are having difficulty developing renewable energy projects. The study will also encourage the investor and corporate sector to join national grid from a business perspective. Furthermore, the scheme will also assist government institutions in reducing their reliance on fuel based plants, so bolstering the state economy and provide clean and cost effective energy. © 2022 IEEE.

关键词： Solar energy

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Performance Analysis & Latency Calculation Of Cooperative Communication Protocols

Performance Analysis & Latency Calculation Of Cooperative Co...

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

作者： Noreen Akram Ayesha Rafique Madiha Abbasi Asif Hasan syed Rehan Ali Zaidi Telecommunication Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Air Engineering Department PNS Mehran Karachi Pakistan

Wireless communications have been remarkably & rapidly evolving during last decade. This research work is focused on the Capacity, Throughput and Outage Probability of Cooperative communication protocols and the total latency caused by Cooperative diversity systems. The objectives of this work are as follows: To calculate the throughput; to calculate the probability; Analysis and evaluations of total latency caused by Cooperative diversity systems. For the research methodology, firstly it is discussed that why cooperation is needed and how cooperation is possible in a wireless network. Then the throughput, outage probability, various types of delays, latency requirement has been discussed. Finally, a system model with single and multiple relays has been presented. For analyzing the performance of Cooperative Communication system, MATLAB tool is used as simulator. The simulation parameters are: Channel gains with additive white Gaussian noise (AWGN); BPSK modulation. Currently most of the work is based on analyzing performance improvement by cooperation but not on its possible side effects. Latency and security needs to be addressed carefully. That is why designing a laboratory based wireless cooperative communication prototype could be the best way to determine how much performance enhancement it can bring in real and practical wireless environment.

关键词： Protocols Cooperative communication Wireless networks Prototypes Probability Throughput Power system reliability

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Comparative Analysis Of Intrusion Detection Systems in SDN

Comparative Analysis Of Intrusion Detection Systems in SDN

引用

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

作者： Rehan Shams Daniyal Omer Suri Fozia Hanif Pablo Otero Department of Telecommunication Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Software Engineer DevOps Motive Karachi Pakistan Department of Mathematics University of Karachi Pakistan Department of Telecommunication Engineering University of Malaga Spain

Networking industry has changed a lot in the past few years and as SDN being the new emerging technology of the future, security concerns have risen enormously. Control plane is separated from data plane in SDN providing the network administrators with more control by pushing application codes into the network also making it more vulnerable to attacks. DoS/DDoS are the most dominant attacks that a network of an organisation faces these days, because of the unsupervised network subscriptions and easily available attack tools/*** research aims to integrate the traditional open source IDS with software Defined Networks and test their capabilities against malicious traffic injected by an attacker. In this paper, we compared two open source IDS Snort and Suricata with SDN and then deliberately injected malicious traffic. We time stamped the DoS/DDoS attack in both scenarios to compare Snort and Suricata true positive and false negative rates. From the results obtained we concluded that used IDS were successfully integrated with a prototype emulated network on Mininet and OpenDaylight being the remote controller and after injecting attacks into the network, it was seen that performance of Snort exceeded than that of Suricata. We propose Snort being the better IDS suited for industry standard deployment.

关键词： Industries Wireless communication Prototypes Network intrusion detection Production Optical fiber networks Hardware

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Abnormalities Detection in Chest CT Scan Images using Federated and Deep Learning

Abnormalities Detection in Chest CT Scan Images using Federa...

引用

International Conference on Emerging Technologies, ICET

作者： Mashab Ali Javed syed Jawad Hussain Aqsa Yasmeen Amaad Khalil Muhammad Bilal Rafaqat Department of Electrical and Computer Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan School of ICT University of Tasmania Hobart Australia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Lung cancer, which accounts for an estimated 2.20 million reported cases and leading to 1.80 million fatalities in the year 2020, represents a significant global cause of mortality. Early detection during Computed Tomography (CT) chest scans can improve survival rate of patients. To address the growing use of CT scan, a robust automated model is required to eliminate tedious manual procedures and reduce variability in diagnosis. Moreover, there are challenges associated with medical research such as data privacy, collaboration, regulatory compliance, and data diversity. In this study, we propose two machine learning (ML) methods and subsequently conduct a comparative analysis. In the first approach, we presented a deep learning technique to achieve high image classification accuracy by using multi-layer CNN and two pre-trained models, namely ResNet50 and DenseNet201. In the second approach, we presented a Federated Learning (FL) technique, which entails the utilization of all three models for classifying abnormalities in chest CT scans. Our results illustrate that the FL-based approach can assist in the diagnosis of anomalies in chest CT scan, while ensuring the privacy of patient data. By exploring the delicate balance between privacy and accuracy in medical image classification, our study will contribute to the advancing realm of research.

关键词： Deep learning Data privacy Privacy Accuracy Federated learning Computed tomography Data models Medical diagnostic imaging Residual neural networks Image classification

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Silent Speech Interfaces: Non-Invasive Neuromuscular Signal Processing for Assistive Communication

Silent Speech Interfaces: Non-Invasive Neuromuscular Signal ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Sallar Khan Khawaja Usman Ali Ali Raza Ali Qadri Maisam Rizvi School of Informatics and Cybersecurity Technological University Dublin Dublin Ireland Department of Computer Science & Information Technology Sir Syed University of Engineering and Technology Karachi Pakistan

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

Our research demonstrates a discrete speech system that empowers those afflicted with speech impairments to convey ideas by internally articulating words without producing audible sound. This method harnesses the minimal movements involved in articulation, making it less arduous than conventional vocalization, especially for individuals facing communication challenges. Silent Speech Interfaces (SSIs) form the foundation of our research, enabling the decoding of neuromuscular signals associated with internal speech articulation to facilitate non-invasive and effective communication for individuals with speech impairments. This research entails the utilization of a multi-electrode configuration to capture subtle, low-voltage electrical signals positioned on the surface area of the subject’s neck. These signals emerge as a consequence of neural activation within the speech articulator muscles, signifying the internal articulation of words within the oral cavity, accompanied by minimal physical movement. Subsequent to signal acquisition, a comprehensive analysis of the data is conducted using a biosensing board (Cyton), facilitated by the implementation of the OpenBCI GUI interface and visualization via Python plotting tools.

关键词： Training Vocabulary Accuracy Neuromuscular Speech recognition Speech enhancement Biosensors Artificial intelligence Usability Standards

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Eye on the Future: Deep Learning with MobileNetV2 for Early Eye Disease Diagnosis

Eye on the Future: Deep Learning with MobileNetV2 for Early ...

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

作者： Shaheena Noor Muhammad Imran Saleem Najma Ismat Aneeta Siddiqui Humera Noor Minhas Hamza Inam Department of Computer Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Department of Software Engineering SSUET Karachi Pakistan Department of Computer Engg. SSUET Karachi Pakistan Quality Filtering Engineering Department eyeo Cologne Germany Avioinics Engineering Pakistan Air Force Islamabad Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

MobileNetV2 represents a significant advancement in the design of efficient neural networks for mobile and embedded applications. Its innovative use of inverted residuals, linear bottlenecks, and depthwise separable convolutions makes it an ideal choice for developers seeking to deploy powerful AI models on resource-constrained devices. This paper presents an efficient algorithm leveraging the MobileNetV2 architecture for the classification of eye diseases, specifically targeting Normal, Cataract, Glaucoma, and Retina Disease categories. The proposed model is designed to perform with high accuracy on resource-constrained devices, making it suitable for mobile and embedded applications in telemedicine and ophthalmology. Extensive testing on a comprehensive dataset has demonstrated the model's effectiveness, achieving precision and accuracy of 96.15% and 97.39%, respectively. This research addresses the growing need for accessible and reliable diagnostic tools in eye care, particularly in remote and underserved regions.

关键词： Performance evaluation Wireless communication Accuracy Telemedicine Transfer learning Eye diseases Retina Robustness Ophthalmology Testing

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