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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario. © 2025, CC BY.

关键词： Markov chains

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rameshwari Kapoor Rashmi Kuliyal Nilesh Bhanot Piyush Bagla Ruchira Rawat Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) University Dehradun Uttarakhand India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70 % on the driver distraction dataset.

关键词： Deep learning Training Analytical models Accuracy Road accidents Transfer learning Convolutional neural networks Driver behavior Vehicles Residual neural networks

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content

Deep Analysis of Machine Learning Techniques for the Effecti...

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Disruptive Technologies (ICDT), International Conference on

作者： Ashish Jain Sudeep Varshney Dept. of Computer Science and Engineering SET Sharda University Greater Noida India Dept. of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms.

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Low Latency Single-Cycle EOG Classification Using Cascaded ANN & CNN

Low Latency Single-Cycle EOG Classification Using Cascaded A...

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International Conference on computer and Information Technology (ICCIT)

作者： Wakim Sajjad Sakib Abdullah Bin Shams Md. Rifatuddin Romel Raisa Tasrin Ridi Mohammad Liton Hossain Dept. of Electronics and Communication Engineering Institute Of Science and Technology Dhaka Bangladesh Dept. of Electrical & Computer Engineering University of Toronto Toronto Canada Dept. of Aeronautical Engineering Military Institute of Science and Technology Dhaka Bangladesh Dept. of Computer Science and Engineering American International University - Bangladesh Dhaka Bangladesh Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Electrooculography (EOG) is a noninvasive method to record the motion of the eye from the electrical potential difference between the cornea and retina. This technique, paired with AI, is extensively utilized predominantly in assistive technologies. The complexity of AI-driven EOG analysis scales with the number and type of expected eye movements. This challenge intensifies when the detection of eye movements relies solely on a single EOG cycle. An essential metric for real-time applications is the latency of an AI algorithm, which refers to the delay in making predictions. The latency should be smaller than the average human visual reaction time for any AI-driven system to promptly respond. The majority of EOG classification studies tend to overlook latency. In this experimental study, we overcome this multi-facet problem by proposing cascaded Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) architectures that have latency smaller than average human reaction time. Also, we developed a generalized & robust signal processing method to correctly identify single EOG cycle and deliver high prediction performances with either ANN or CNN. To demonstrate, we successfully classified nine distinct eye movements based only on single cycle EOG signal, with the performance metrics reaching close to 100% for both ANN & CNN, and latency well below the reaction time.

关键词： Measurement Electrooculography Wheelchairs Signal processing algorithms computer architecture Prediction algorithms Real-time systems Classification algorithms Convolutional neural networks Artificial intelligence

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Inductive Program Synthesis via Iterative Forward-Backward Abstract Interpretation

arXiv

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arXiv 2023年

作者： Yoon, Yongho Lee, Woosuk Yi, Kwangkeun Seoul National University Dept. of Computer Science & Engineering Korea Republic of Hanyang University Dept. of Computer Science & Engineering Korea Republic of

A key challenge in example-based program synthesis is the gigantic search space of programs. To address this challenge, various work proposed to use abstract interpretation to prune the search space. However, most of existing approaches have focused only on forward abstract interpretation, and thus cannot fully exploit the power of abstract interpretation. In this paper, we propose a novel approach to inductive program synthesis via iterative forward-backward abstract interpretation. The forward abstract interpretation computes possible outputs of a program given inputs, while the backward abstract interpretation computes possible inputs of a program given outputs. By iteratively performing the two abstract interpretations in an alternating fashion, we can effectively determine if any completion of each partial program as a candidate can satisfy the input-output examples. We apply our approach to a standard formulation, syntax-guided synthesis (SyGuS), thereby supporting a wide range of inductive synthesis tasks. We have implemented our approach and evaluated it on a set of benchmarks from the prior work. The experimental results show that our approach significantly outperforms the state-of-the-art approaches thanks to the sophisticated abstract interpretation techniques. © 2023, CC BY.

关键词： Iterative methods

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Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

Analyzing Yelp Open-Source Data-Set in Azure Data-bricks

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International Conference on Inventive Computation Technologies (ICICT)

作者： Mugunth D Maheshwaran R Maheswari M S. L. Jany Shabu J. Refonaa Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

A data-driven application, Yelp has always served and will always serve as one. It is one of the first companies that allows local businesses to receive reviews from their customers. Communities have always developed it in cooperation with each other. According to Yelp, the data-set contains information regarding businesses, reviews, users, and check-ins, and has continuously been updated since 2015. A project titled "Analyzing Yelp Open-Source Data-set in Azure Data Bricks" will analyze this data-set which has been made available as an open source for sentiment analysis and descriptive analysis. This study analyzes local business performance, business distribution, review ratings, and other factors, as well as check-in rates in American business locations over time. This analysis shows that the Yelp is losing reviews, tips, elite users, and check-ins.

关键词： Sentiment analysis Reviews Decision making Data visualization Machine learning Companies Data science

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Transfer Learning in Deep Neural Network for Land Cover Classification 25

Transfer Learning in Deep Neural Network for Land Cover Clas...

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25th International Conference on computer and Information Technology, ICCIT 2022

作者： Sadia, Amina Uddin, S. M. Shihab Islam, Rafiqul University of Chittagong Institute of Forestry and Environmental Sciences Bangladesh Islamic University of Technology Dept. of Cse Bangladesh Dhaka University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350346022

Land cover classification research contributes significantly to sustainable development by enhancing the baseline for urban planning, natural resource management, and environmental monitoring at the local, regional, national, and global levels. In practice, only the RGB and near-infrared (NIR) image bands are obtainable for identifying land cover since high-resolution satellite remote sensing data may not even be accessible due to cost, storage systems, or bandwidth restrictions. This work presents Transfer Learning in Deep Neural Networks (DNN) for land cover classification, where ResNet101 is considered as a DNN model. Numerical experiments are conducted using the EuroSAT dataset, a multispectral dataset based on Sentinel-2 satellite images of 34 European cities covering 13 spectral bands in visible, near-infrared, and short-wave infrared parts. Several experiments were conducted, and the proposed ResNet101-based technique effectively classifies the land cover classes with more than 99% accuracy. © 2022 IEEE.

关键词： Deep neural networks

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Social engineering attack types and prevention techniques- A survey 1

Social engineering attack types and prevention techniques- A...

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1st International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Bhatia, Shruti Yadav, Satya Prakash Near East University Ai and Robotics Institute Research Center for Ai and IoT Department of Computer Engineering Nicosia Turkey Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

The term "Cyber Security"is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim's social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article. © 2023 IEEE.

关键词： Crime

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Conditionals Support in Binary Expression Tree Based Genetic Programming 4

Conditionals Support in Binary Expression Tree Based Genetic...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Chang, Feng-Cheng Huang, Hsiang-Cheh Tamkang University Dept. of Computer Science and Information Engineering Taiwan National University of Kaohsiung Department of Electrical Engineering Kaohsiung City Taiwan

ISBN: (纸本)9781665419048

Inspired by the genetic algorithm (GA), the genetic programming (GP) was proposed for searching a program that fits a certain behavior. There are many aspects that distinguish GP from GA a lot, though GP concepts were originating from GA. In this paper, we focus on the representation scheme for a GP program. A GP program contains both operators and operands. Without proper encoding, the GP crossover and mutation are likely to produce invalid programs. Based on our previous design experiences, we proposed an alternative approach. It is a binary expression tree based representation with conditional behavior of each node. Therefore, the scheme supports unary, binary, and ternary operators. It also reduce the probability of producing invalid programs. A feature of the scheme is that conditional operators are first-class member because each evaluation embeds conditional processing. A few image-processing experiments were conducted to show the effectiveness of the design. The experimental results are also discussed in this paper. © 2022 IEEE.

关键词： Genetic algorithms

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Leveraging Hallucinations to Reduce Manual Prompt Dependency in Promptable Segmentation 38

Leveraging Hallucinations to Reduce Manual Prompt Dependency...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jian Lin, Jiayi Yan, Junchi Gong, Shaogang School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Dept. of CSE School of AI Moe Key Lab of AI Shanghai Jiao Tong University China

Promptable segmentation typically requires instance-specific manual prompts to guide the segmentation of each desired object. To minimize such a need, task-generic promptable segmentation has been introduced, which employs a single task-generic prompt to segment various images of different objects in the same task. Current methods use Multimodal Large Language Models (MLLMs) to reason detailed instance-specific prompts from a task-generic prompt for improving segmentation accuracy. The effectiveness of this segmentation heavily depends on the precision of these derived prompts. However, MLLMs often suffer hallucinations during reasoning, resulting in inaccurate prompting. While existing methods focus on eliminating hallucinations to improve a model, we argue that MLLM hallucinations can reveal valuable contextual insights when leveraged correctly, as they represent pre-trained large-scale knowledge beyond individual images. In this paper, we utilize hallucinations to mine task-related information from images and verify its accuracy for enhancing precision of the generated prompts. Specifically, we introduce an iterative Prompt-Mask Cycle generation framework (ProMaC) with a prompt generator and a mask generator. The prompt generator uses a multi-scale chain of thought prompting, initially exploring hallucinations for extracting extended contextual knowledge on a test image. These hallucinations are then reduced to formulate precise instance-specific prompts, directing the mask generator to produce masks that are consistent with task semantics by mask semantic alignment. The generated masks iteratively induce the prompt generator to focus more on task-relevant image areas and reduce irrelevant hallucinations, resulting jointly in better prompts and masks. Experiments on 5 benchmarks demonstrate the effectiveness of ProMaC. Code given in https://***/ProMaC/. © 2024 Neural information processing systems foundation. All rights reserved.

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