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Detecting Helmets Of The Bike Riders Using Deep Learning Algorithms 14

Detecting Helmets Of The Bike Riders Using Deep Learning Alg...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mia, Yousuf Rahman, Md. Solaimanur Basak, Animesh Hossain, Sheikh Mufrad Zulfiker, Md. Sabab Sumaia, Merina Akter Daffodil International University Dept. of Computer Science & Engineering Dhaka Bangladesh Model Institute of Science & Technology Dept. of Computer Science & Engineering Gazipur Bangladesh

ISBN: (纸本)9798350335095

We propose real-time bike helmet detection in our study. A lot of people ride bikes in our nation. Motorbikes are more popular than vehicles because they are cheaper to maintain, take up less parking space, and provide more mobility and adaptability in urban situations. Bike riding is entertaining yet risky. Bicyclist safety is the planned system's main purpose. Many drivers don't wear helmets even though they're mandated by law. In emerging countries, mortality has been growing steadily. A helmet detection technology that identifies drivers without helmets is needed to safeguard the public. We employ a real-time 3202 dataset for this approach. We gather wearing helmet 1911 and no helmet 1291 data and utilize algorithms like VGG16, Resnet50, MobileNet v.02, Inception V3, EfficientNet, and CNN. The EfficientNet achieved 98% accuracy. Each person's comparison statement techniques are in the implementation section. To construct the optimum model for the conditions, this inquiry uses model validation approaches. © 2023 IEEE.

关键词： Deep learning

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Detecting Ransomware Threats Using Machine Learning Approach 8

Detecting Ransomware Threats Using Machine Learning Approach

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shetty, Sanmitha Chitharanjan Bastian, Rose Khader, Sarah Abdul St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Hybrid Deep Neural Network for Early Detection and Classification of Chicken Diseases 5

A Novel Hybrid Deep Neural Network for Early Detection and C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Uddin, Md Shihab Islam, Md Fakrul Rahman, Sharukh Foysal, Nasir Uddin Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka1216 Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350394290

Detection and classification of chicken diseases has been a longstanding concern for farmers. Relying solely on expert opinions could be a costly option. Moreover, there are certain chicken diseases that spread quickly, and necessary procedures are immediately needed to carryon. On the other hand, the lack of research activity or low availability of data on this topic leads to many misunderstandings and challenges so as to correctly detect a certain type of chicken disease. The existing techniques show low accuracy on disease classification, which leads to a complex engineering problem and critical analysis is required for sustainable development. In this paper, we develop a novel hybrid deep neural network, namely ChickenNet21, which combines a Convolutional Neural Network (CNN) and a fine-tuned Visual Geometry Group (VGG) model to address this challenge. Using a large dataset of 6,812 farm-labeled fecal images, we assess the effectiveness of ChickenNet21 and it achieves 98.83% accuracy in identifying chicken diseases. © 2023 IEEE.

关键词： Convolutional neural networks

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Software Code De-Compilation Techniques and Approaches: A Comparative Study 25

Software Code De-Compilation Techniques and Approaches: A Co...

引用

25th International Arab Conference on Information technology, ACIT 2024

作者： Fawareh, Hamed Al-Smadi, Yazan Hassan, Mohammad Fawareh, Faid AlNoor Elrashidi, Ali Zarqa University Dept. of Software Engineering Jordan Zarqa University Dept. of Computer Science Jordan Zarqa University Dept. Of Mathmitical Science Jordan University of Business and Technology Dept. of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

De-compilation is the process of executable files as input where the de-compilation process tries to get away the source code, or the majority of it, if the file's source code is lost or damaged for whatever cause. De-compilation has many uses such as code recovery, security testing, and errors corrections, however, de-compilation is cautious since it depends on certain approaches and techniques. Nowadays de-compilation done by many automated and semi-automated tools, but the accuracy and the efficiency of it depend on the proposed approaches and techniques to stand in front of reengineering challenges. This paper aims to highlight up-to-date prior contributions in the field where 8 published research papers from May 2020 until May 2023 were collected to investigate the state-of-art. As a result, some of the researchers tend to develop their own tools and others tend to use popular automated with different approaches and techniques where the majority of the researchers focused on C, C++, and JAVA programming languages where 50% were de-compilation on C language. © 2024 IEEE.

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Empowering Android Malware Detection: A Deep Learning Ensemble with Optimal Features 26

Empowering Android Malware Detection: A Deep Learning Ensemb...

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

作者： Shakil, Mehedi Hasan Hasan, Md. Mynul Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798350359015

Because of the rapid expansion of mobile devices and the growing reliance on mobile applications, there has been an increase in Android malware threats, necessitating the development of sophisticated detection systems. This research proposes a technique for detecting Android malware that makes use of several deep learning models. The goal is to provide an accurate and dependable method for classifying mobile applications as malicious or benign. To assure data integrity, the Drebin dataset is preprocessed. RFECV was employed to obtain the optimal features for deep learning algorithms such as Bi-LSTM, Bi-GRU, and 1D CNN. The dataset was trained using the selected features for each deep learning algorithm based on the feature selection method. The ensemble technique, employing a weighted average method, was utilised to combine the predictions of the individual models. Extensive testing revealed that combining Bi-LSTM and CNN in the ensemble technique achieves the greatest accuracy of 98.99%. This combination highlights the efficiency of combining various models strengths for Android malware detection. © 2023 IEEE.

关键词： Bi-Directional Gated Recurrent Unit (Bi-GRU) Bi-Directional Long Short Term Memory (Bi-LSTM) Convolutional Neural Network (CNN) Recursive Feature Elimination with Cross-Validation (RFECV) weighted average ensemble

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A Technique to Avoid Blockchain Denial of Service (BDoS) and Selfish Mining Attack 5

A Technique to Avoid Blockchain Denial of Service (BDoS) and...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Habib, Md. Ahsan Hossen Manik, Md. Motaleb Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350339239

Blockchain denial of service (BDoS) and selfish mining are the two most crucial attacks on blockchain technology. A classical DoS attack targets the computer network to limit, restrict, or stop accessing the system of authorized users which is ineffective against renowned cryptocurrencies like Bitcoin, Ethereum, etc. Unlike the conventional DoS, the BDoS affects the system's mechanism design to manipulate the incentive structure to discourage honest miners to participate in the mining process. In contrast, in a selfish mining attack, the adversary miner keeps its discovered block private to fork the chain intentionally that aiming to increase the incentive of the adversary miner. This paper proposed a technique to successfully avoid BDoS and selfish mining attacks. The existing infrastructure of blockchain technology does not need to be changed a lot to incorporate the proposed solution. © 2023 IEEE.

关键词： Machine design

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Preprocessing Pathology Reports for Vision-Language Model Development

Preprocessing Pathology Reports for Vision-Language Model De...

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2024 MICCAI Workshop on Computational Pathology, MICCAI COMPAYL 2024

作者： Lucassen, Ruben T. van de Luijtgaarden, Tijn Moonemans, Sander P.J. Blokx, Willeke A.M. Veta, Mitko Dept. of Pathology University Medical Center Utrecht Netherlands Dept. of Biomedical Engineering Eindhoven University of Technology Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Pathology reports are increasingly being used for development of vision-language models. Because the reports often include information that cannot directly be derived from paired images, careful selection of information is required to prevent hallucinations in tasks like report generation. In this paper, we present a language model for subsentence segmentation based on the information content, as part of a preprocessing workflow for 27,500 pathology reports of cutaneous melanocytic lesions. After initial clean up, the reports were first translated from Dutch to English and then segmented by separate language models. Both models were developed using an iterative approach, in which the development dataset was expanded with manually corrected model predictions for previously unannotated reports before finetuning the next version of the models. Over the course of eight iterations, the development dataset was in the end scaled up to 1,500 translated and annotated reports. On the independent test set of 3,597 sentences from 150 reports, 219 translation errors (6,1%) of different severities were counted. The subsentence segmentation model achieved a strong predictive performance on the test set with a macro average F1-score of 0.921 (95% CI, 0.890-0.940) and a weighted average F1-score of 0.952 (95% CI, 0.944-0.960) over 13 different classes. The remaining 25,850 unannotated reports were translated and segmented using the final models to complete the dataset preprocessing. Differences in word count and class distribution between section types of the reports were explored in preparation for future vision-language modeling. The presented methodology is generic and can, therefore, easily be extended to multiple or different pathology domains beyond melanocytic skin lesions. Code and trained model parameters are made publicly available. ©2024 Lucassen et al.

关键词： Visual languages

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Internet of Things and Artificial Intelligence Enabled Smart Classroom 6

Internet of Things and Artificial Intelligence Enabled Smart...

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6th International Conference on Electrical Information and Communication technology, EICT 2023

作者： Akter, Sharmin Asaduzzaman, Asaduzzaman Chittagong University of Engineering & Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798350370836

Smart classrooms are now a buzzword in the field of education, both at home and abroad. With the advancement of technology, everything is getting automated and faster. Classrooms are not stepping behind either. In this work, a smart classroom is developed with several important features, like the total number of students in the classroom, a geolocation-based individual students' attendance system, and student attentiveness detection using eye blink *** prediction of the number of students is done using Machine Learning-based techniques based on environmental sensor data. The data collection module is designed using CO2, temperature, and humidity sensors. A dataset is prepared by collecting real-time data and building a multiclass prediction model based on sensor data to predict the number of students. The individual students' attendance system is developed using the existing WiFi system around the classroom, where students are connected to the WiFi network from their registered mobile devices. The geographical location of their devices is compared with the classroom location. A noble approach is also proposed to monitor the attentiveness of students by their eye blink rate, which is also monitored on the mobile device available to them. The results are shown to validate the effectiveness of the proposed systems. Finally, all the features are integrated into a graphical user interface. © 2023 IEEE.

关键词： Students

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An Efficient Lightweight Cryptosystem Using Substitution-Shuffling Based Key Generation 26

An Efficient Lightweight Cryptosystem Using Substitution-Shu...

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

作者： Provat, Al-Anher Haque, Md. Mokammel Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

In recent years, there has been a meteoric rise in the use of small computing devices, including RFID (Radio Frequency Identification) tags, wireless sensors, embedded electronics, and IoT (Internet of Things) gadgets. Given that these gadgets handle highly sensitive data, their protection should be a top priority. Therefore, it is crucial to implement a secure encryption system on susceptible devices. Standard encryption algorithms like RSA and AES are resource-intensive, require substantial memory, and can slow down devices. In this work, we introduce a lightweight cryptosystem that is suitable for low-powered devices without compromising security. This system utilizes Substitution-Permutation network, incorporating a variation of the Feistel design, to establish a secure symmetric key block cipher. As an enhancement over the standard technique, the proposed algorithm requires less run-time while still providing sufficient protection to maintain an avalanche effect of nearly 50%. © 2023 IEEE.

关键词： Block ciphers GFN IoT Knuth shuffle MATLAB S-box SPN

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Ink Detection from Carbonized Herculaneum Papyri using Deep Learning 26

Ink Detection from Carbonized Herculaneum Papyri using Deep ...

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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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