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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Mou, Farzana Sharmin Ahmed, Tanvir University of Birmingham Dept. of Computer Science Birmingham United Kingdom Dhaka University of Engineering & Technology Gazipur Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

A Roman villa in the town of Herculaneum, next to Pompeii, housed a library with thousands of ancient Greek and Roman scrolls. The eruption of Mount Vesuvius buried this villa approximately 2000 years old ago which has been discovered. The scrolls can no longer be opened without breaking them because they were carbonized by the heat of the volcano. Since Herculaneum ink is carbon-based, it affords no X-ray contrast against the underlying carbon-based papyrus. Some detached papyrus fragments that can be read under infrared light can be used as ground truth data for a machine learning model that could identify otherwise undetectable ink from X-rays. The process of ink detection involves locating the inked portions of the papyrus using data from a 3D X-ray scan of the papyrus surface. The model has been trained by selecting a pixel from the binary label picture and sampling a subvolume from the surface volume at the same coordinates. In order to update the model weights, then backpropagate the known label data. Once trained, the model can be used to predict the appearance of a label picture from diverse input data. Three distinct models, namely U-Net, Attention U-Net, and a Pretrained Autoencoder with Attention U-Net, have been applied. Then, utilizing those three models, ensemble approaches have been employed (average, voting, and weighted average) to choose the optimum outcome. The best dice score of 0.766 has been achieved by the average ensembling technique. This project shows how machine learning can detect ink in 3D scans and decipher carbonized parchment contents. © 2023 IEEE.

关键词： attention mechanism computer vision ensemble technique ink detection pretrained autoencoder unet

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Unveiling DNS Spoofing Vulnerabilities: An Ethical Examination Within Local Area Networks 26

Unveiling DNS Spoofing Vulnerabilities: An Ethical Examinati...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Jony, Asaduzzaman Islam, Muhammad Nazrul Sarker, Iqbal H. Dept. of Computer Science and Engineering Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Edith Cowan University PerthWA-6027 Australia

ISBN: (纸本)9798350359015

Nowadays DNS spoofing has become one of the terrible attacks which is triggered by exploiting IP address conflict detection vulnerability on the DHCP server side. DHCP server running on latest operating systems can detect IP address conflict which makes proposed attack ineffective. On the other hand, DNS spoofing attack using server side DNS cache poisoning is very difficult and unfeasible task as the public DNS servers are very secured and deployed behind several security appliances. Again, DNS spoofing attack is initiated by poisoning the client's DNS cache, which is uncertain as it requires a malware to be installed in target user's computer. Therefore, the objective of this study is to introduce an innovative approach for ethically launching a DNS spoofing attack within a Local Area Network (LAN) for illustrating the vulnerabilities and security flaws of the DNS protocol. To achieve this objective, attacker introduces a rogue DHCP server in the LAN and forcefully redirects user's traffic to a malicious or rogue default gateway. Secondly, a python (scapy) based DNS spoofing engine is deployed in the rogue DHCP server that tampers the DNS responses replied by the DNS server. The scapy alters the IP address of legitimate website with an IP address of malicious or phishing website which is managed by the attacker and looks like legitimate website. As a result, compromised user is redirected to that identical looking phishing website and becomes victim of phishing attack. Finally, the effectiveness and validation of the proposed attack were demonstrated using the Kali Linux and multi-vendor network emulation software (EVE-NG). © 2023 IEEE.

关键词： DNS Spoofing Ethical Considerations LAN security Phishing Python Scapy Rogue DHCP Wireshark

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An IoT-Based Health Care Solution: A Smart Medical Box 4

An IoT-Based Health Care Solution: A Smart Medical Box

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4th Asia Service sciences and Software engineering Conference, ASSE 2023

作者： Khan, Adib Mahamud Kabir, Raihan Sudha, Nigar Sultana Ahmed, Nadeem Watanobe, Yutaka Islam, Md Rashedul Dept. of Computer Science and Engineering University of Asia Pacific Dhaka1216 Bangladesh Dept. of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima Japan

ISBN: (纸本)9798400708534

IoT in healthcare is intended to keep people safe and healthy, with the primary goal of lowering healthcare costs and reducing the involvement of caregivers in the coming years. This research proposes a smart IoT-based health- care system, which includes an intelligent medical box connected to a mobile app for continuous health monitoring. Patients can get regular health care with the help of this medical box with a dedicated app and web server. The proposed system helps the patients to check their heart rate, temperature, and oxygen level, all using just one device. It provides an easy way for the user to do this checkup by themselves without any help from caregivers or even doctors. Moreover, the proposed system assists the patients in taking the proper medicinal drugs in due time with an SMS and buzzer alarm. It additionally creates a clean verbal exchange between medical doctors and patients rather than bodily contact. The proposed system can be accessed through an app that stores all the detailed health reports about the patient. Doctors can easily check the patient's report and can change the prescriptions based on the condition of the reports, if necessary. This box also consists of an emergency button that the user can use in case of any emergency to notify their respective caregivers immediately. © 2023 ACM.

关键词： Electrocardiograms

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COVID-19 Image Retrieval Using Siamese Deep Neural Network and Hashing Technique 14

COVID-19 Image Retrieval Using Siamese Deep Neural Network a...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Boroujeni, Farsad Zamani Afshari, Doryaneh Hossien Mahmoodi, Fatemeh Science and Research Branch Dept. of Computer Engineering Tehran Iran Branch Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

Medical images are crucial for early diagnosis and are increasingly important in modern medicine. This study presents a medical image retrieval system using a Siamese neural network with 13 layers. The system uses the Minimum Redundancy Maximum Relevance (mRMR) technique to extract deep features from the Siamese, and then uses binary hashing to retrieve similar images using Hamming distance. The experimental results on Covid-19 dataset show that the proposed method improves lung image retrieval by over 6% compared to previous methods, achieving an average accuracy of 93.83% and 92.73% in 5 and 10 retrieved images, respectively. © 2023 IEEE.

关键词： Image enhancement

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DeepLeaf-Attentive: Integrating Attention Mechanisms for Crop Leaf Disease Identification 26

DeepLeaf-Attentive: Integrating Attention Mechanisms for Cro...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Tanvir Mou, Farzana Sharmin Engineering Dhaka University of Engineering & Technology Dept. of Computer Science Dhaka Gazipur Bangladesh University of Birmingham Dept. of Computer Science Birmingham United Kingdom

ISBN: (纸本)9798350359015

Crop leaf diseases have a detrimental impact on agricultural production. Visual inspection by organic farmers for disease detection is time-consuming and ineffective. To address this, we propose DeepLeaf-Attentive, a novel deep learning model for classifying crop leaf diseases. It combines MobileNet architecture with additional layers and attention mechanism. It is trained on a dataset of 30,000 images including data augmentation representing 60 classes with 500 images per class. The model achieves outstanding performance, with 97.21% accuracy, 97.72% precision, 97.43% recall, 97.49% F1-score and 18.0 ms inference time. Comparative evaluations against state-of-the-art approaches demonstrate the superiority of DeepLeaf-Attentive in crop leaf disease classification. By providing a reliable and efficient tool, our model will empower farmers to accurately identify and categorize various crop leaf diseases, facilitating informed decision-making and enabling the implementation of targeted and timely treatment strategies. The adoption of DeepLeaf-Attentive can significantly improve disease management practices in agriculture, leading to enhanced crop yields, reduced economic losses, and sustainable farming practices. By revolutionizing crop leaf disease identification, our research contributes to the advancement of precision agriculture, benefiting farmers and supporting global food security. © 2023 IEEE.

关键词： Attention Mechanisms Crop Leaf Disease Identification Deep Learning DeepLeaf-Attentive MobileNet Transfer Learning

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YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesion Classification Using the HAM10000 dataset

YOLOv8-Based Deep Learning Approach for Real-Time Skin Lesio...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Saha, Utsha Ahamed, Imtiaj Uddin Imran, Md Ashique Ahamed, Imam Uddin Hossain, Al-Amin Gupta, Ucchash Das Dept. of Computer Science North Dakota State University FargoND United States Agricultural Data Analytics Unit North Dakota State University FargoND United States Dept. of Computer Science University of South Dakota VermillionSD United States Dept. of Applied Computer Science The University of Winnipeg Winnipeg Canada Dept. of Computer Science Liverpool John Moores University Liverpool United Kingdom

ISBN: (纸本)9798350350548

Skin cancer is a prevalent and potentially fatal disease that requires early detection for effective treatment. We trained and evaluated five YOLOv8 classification model variants (YOLOv8n-cls, YOLOv8s-cls, YOLOv8m-cls, YOLOv8l-cls, and YOLOv8x-cls) on the HAM10000 dataset, which contains 10,015 dermatoscopic images of common pigmented skin lesions. The models were trained for 30 epochs using data augmentation techniques to enhance generalization. Performance was assessed using metrics including accuracy, precision, recall, F1-score, and inference time. The YOLOv8x-cls model achieved the highest accuracy of 86.2% and precision of 82.1%, while the YOLOv8lcls model demonstrated the best balance with the highest F1-score of 77.0%. Compared to previous ensemble approaches, our single YOLOv8 models achieved superior performance with lower computational overhead. The YOLOv8n-cls variant showed the fastest inference time of 0.5 ms, making it suitable for real-time applications. Our results demonstrate the potential of YOLOv8-based models for accurate and efficient skin lesion classification, which could aid in early skin cancer detection and improve patient outcomes. © 2024 IEEE.

关键词： Lung cancer

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Optimal Scheduling in Multiserver Queues

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Performance Evaluation Review 2024年第3期51卷 29-32页

作者： Grosof, Isaac Carnegie Mellon University Computer Science Department United States Northwestern University Dept. of Industrial Engineering & Management Sciences United States

Scheduling theory is a key tool for reducing latency (i.e. response time) in queueing systems. Scheduling, i.e. choosing the order in which to serve jobs, can reduce response time by an order of magnitude with no additional resources. Scheduling theory is well-developed in single-server systems, where one job is processed at a time. However, little is known about scheduling in multiserver systems, where many jobs are processed at once. Results are especially limited in stochastic multiserver scheduling theory. Today’s data-centers have thousands of servers, and scheduling theory is unable to analyze such systems. My thesis proves the first optimality results and first closed-form bounds on mean response time for scheduling policies in stochastic multiserver models which reflect the behavior of modern computing systems. My thesis proves novel optimality results in each of three areas: I start by studying one-server-per-job multiserver models, and prove the first results on optimal scheduling in that setting. Next, I study the multiserver-job (MSJ) model, where different jobs require different amounts of resources to be served. I prove the first characterization of mean response time for any scheduling policy in the MSJ model, as well as the first optimality results. Finally, I study the effects of scheduling on the tail of response time, rather than mean response time. I invent a novel scheduling policy, Nudge, which I prove to be the first policy to outperform FCFS’s asymptotic tail of response time. This extended abstract briefly outlines the main contributions of my thesis, describing my novel performance bounds and optimality results in each of these three areas. © 2024 Association for Computing Machinery. All rights reserved.

关键词： Stochastic systems

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements 5

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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5th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2024

作者： Hanna, Grace Boyar, Nathan Garay, Nathan Maleki, Mina University of Detroit Mercy Dept. of Electrical and Computer Engineering and Computer Science DetroitMI United States

ISBN: (纸本)9798350355925

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation. © 2024 IEEE.

关键词： Software design

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qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture 32

qCrop: An IoT Based Framework to Enhance Crop Productivity i...

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32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Shamsa, Mahdi Rachakonda, Laavanya Mohanty, Saraju P. Kougianos, Elias University of North Texas Dept. of Computer Science and Engineering Denton United States University of North Carolina Wilmington Dept. of Computer Science Wilmington United States University of North Texas Dept. of Electrical Engineering Denton United States

ISBN: (纸本)9798331539672

Food is crucial in our life. Despite food manufacturers' efforts to meet consumers' needs with manufactured food, it cannot attain the identical level of quality and flavor as natural food. Some fruits and vegetables are classified as food that cannot be manufactured. Also, they play an essential role worldwide. This paper focuses on how farmers can protect their farms from an object that might threaten the crops and cause damage to them. It proposes a framework that farmers may utilize to safeguard the crops against any species of birds. Considering the adverse impact of bird attacks on crop production, this system effectively addresses this problem and consistently enhances the quality and quantity of them. It utilizes computer vision technology to establish a secure environment for the crop. This system is called Quality of Crop Device (qCrop), and it works with a You Only Look Once (Yolov8m) model to detect birds with high accuracy to protect farms. © 2024 IEEE.

关键词： Agri-food

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Utilizing Advanced Machine Learning Techniques for Effective Prediction and Control of Pandemic Outbreaks 2

Utilizing Advanced Machine Learning Techniques for Effective...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Rajasekaran, R. Thalapathi Shanmugavalli, H. Jayalakshmi, P. Ambika, S. Balaji, V. Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India Sri Krishna College of Engineering Dept. of Computer Science and Engineering Arakkonam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India

ISBN: (纸本)9798350384277

The health and social implications of pandemic epidemics are substantial. Accurate forecasting and management of such epidemics are of utmost importance to lessen the blow of such epidemics. To tackle this pressing problem, we employ state-of-the-art machine learning methods in our research. In this paper, we investigate the potential of cutting-edge algorithms for modeling, predicting, and controlling the spread of pandemics. These algorithms include LSTM, Random Forest, GNN, RL, DRL, Bayesian Networks, and SVM. We run a battery of experiments and analyses using real-world data sources, such as COVID-19 case data, mobility data, contact tracking data, vaccination distribution data, intervention strategy data, and resource allocation data. These advanced machine-learning approaches have the ability to enhance the accuracy of pandemic predictions, optimize the allocation of resources, decrease transmission rates, and enable more effective intervention measures, according to our findings. For example, our RL algorithm optimized vaccination distribution by 20% while our LSTM model averaged a 92% success rate in forecasting new cases each day. Our research further emphasizes the significance of early detection and intervention. With a 90% success rate, the SVM-based early detection system could spot epidemics ten days before conventional approaches could, buying precious time for focused and efficient responses. © 2024 IEEE.

关键词： COVID-19

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