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An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural Network Technique for Event Detection in Video Sequences 1

An Adaptive Nature-Inspired Paticle Bee Colony Deep Neural N...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Sachdeva, Kumud Sandhu, Jasminder Kaur Sahu, Rakesh Chandigarh University Dept. of Computer Science Engineering Punjab Gharuan140413 India

ISBN: (纸本)9798350361780

Classifying and detecting difficult video events based on visual modalities remains an uncertain problem. Conventional video presentation approaches ineffectively extract each modality, hindering video event detection (VED) rates. The research methodology involves pre-processing steps: uploading video data samples, extracting video frames, converting to grayscale (rgb2gray), enhancing, and calculating smooth frames from the UCF -101 dataset. This pre-processing phase aims to deliver high-quality frames without data loss. Next, the feature extraction process employs the HoG method to extract feature vectors from refined video frames, facilitating the training process and efficiently reducing dimensions. An adaptive, nature-inspired PBC method is then implemented to select reliable and optimized feature sets from the extracted ones. These selected optimized feature vectors are input into different events of the deep neural network (DNN) classifier. Finally, reliable feature sets are identified through feature matching, and experimental outcomes demonstrate a significant 21.3% enhancement in VED and classification, assessed through accuracy rate, specificity (SP), sensitivity (SN), etc. Compared with traditional approaches using manually designed feature sets, the proposed approach proves more effective. Simulation outcomes on publicly available VED databases consistently outperform state-of-the-art video representation methods such as EFS-linear multi-support vector machine (MSVM), convolutional neural network (CNN), etc. © 2024 IEEE.

关键词： Deep neural networks

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Dirt Identification and Removal based on Non-Contact Infrared Temperature Sensor on Solar Panel 7

Dirt Identification and Removal based on Non-Contact Infrare...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rupin, Rezmah Jemima Hasan, Imran Jahan, Rownak Haque Ontor, Md. Rakibul Islam, Md. Motaharul East West University Dept. of Computer Science Engineering Dhaka Bangladesh United International University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341485

Temperature sensing is a technique to detect dirt on a solar-panel-surface that can influence its surface temperature and can reduce its performance, which necessitates monitoring to optimize solar panel output generation, reduce unnecessary costs, and perform efficient cleaning. Temperature sensing can achieve faster and more accurate detection of dirt without using any robotic cameras or manual observation. In this study, a method is proposed to detect dirt on a solar panel with the help of an MLX90614 sensor, a DHT22 sensor connecting with Arduino UNO components. DC motors and gear motors are being used for the removal of accumulated dirt. The temperature of the solar panel surface is measured by an MLX90614 sensor using infrared radiation, while the environment's humidity and temperature are measured by a DHT22 sensor. The system implies measuring the solar panel's temperature and comparing it to the ambient temperature. This system aims at improving previously proposed dirt detection methods using this approach and suggests a motor-driven dirt removal technique. © 2023 IEEE.

关键词： Solar panels

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Improving Time Series Forecasting Accuracy with Transformers: A Comprehensive Analysis with Explainability 3

Improving Time Series Forecasting Accuracy with Transformers...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Joseph, Susmera Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science Engineering Kottayam India

ISBN: (纸本)9798350369083

Time series forecasting is crucial in numerous sectors, including healthcare, energy, and finance. Transformer models, initially designed for natural language processing applications, demonstrate potential in capturing temporal dependencies within sequential data. In this paper, the application of transformer models in time-series forecasting is explored, and their performance and interpretability are investigated. Transparent models build trust and facilitate decision-making, especially in industries with ethical guidelines. While inherently interpretable time-series Transformers exist, there is a gap in research regarding post hoc methods, which is an area ripe for exploration. Understanding and comparing complex Transformer models for time series forecasting can benefit from advancements in explainability, ensuring accurate and comprehensible predictions. In comparison to five other transformer-based models and classical models, the Temporal Fusion Transformer (TFT) model outperformed them in short-term forecasting. Further enhancement of the model performance was achieved through a meticulous analysis of the Encoder-Decoder's importance, which contributed significantly to refining the forecasting accuracy. The vanilla transformer was explained using permutation feature importance. The model stability was analyzed using perturbation methods. © 2024 IEEE.

关键词： Spatio-temporal data

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Architectural Design for AdhaarAssist - A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist - A Forward-Looking Lo...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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A Deep Learning Approach to Enhance Air Quality Prediction: Comparative Analysis of LSTM, LSTM with Attention Mechanism and BiLSTM

A Deep Learning Approach to Enhance Air Quality Prediction: ...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Bhardwaj, Deepanshu Ragiri, Prakash Rao Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350364866

In this study, we address the challenge of predicting air quality indices (AQIs) by leveraging Long Short-Term Memory (LSTM) networks. Recognizing the dynamic nature of air quality data, we explore the efficacy of three LSTM variants - standard LSTM, LSTM with an attention mechanism, and Bidirectional LSTM (BiLSTM) - in capturing temporal dependencies and improving forecast accuracy. Our methodology involved preprocessing a comprehensive dataset of air quality measures, followed by an experimental design that tested each model variant under different sequence lengths (30 and 365 days). The models were evaluated based on their mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) across three response variables: Nitrogen Oxides (NO), Particulate Matter up to 10 micrometers in size (PM10), and Particulate Matter up to 2.5 micrometers in size (PM2.5). Notably, when comparing the RMSE values across the three models, the LSTM with the attention mechanism consistently outperformed the standard LSTM and BiLSTM models, particularly with a sequence length of 30 days, indicating a superior ability to capture the intricacies of temporal dependencies in environmental data. Moreover, the shorter sequence length generally yielded better predictive performance, suggesting that longer sequences might introduce unnecessary complexity or overfitting. These indications of our study suggest a promising direction for the deployment of attention-based models in real-time AQI prediction systems, which could enhance the accuracy of environmental forecasts and inform public health decisions more effectively. Future work will explore the integration of additional environmental factors, the refinement of model frameworks, and the expansion of the dataset to include multi-city comparisons. © 2024 IEEE.

关键词： Mean square error

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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

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8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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Detection and Localization of Car Damages Using Deep Learning

Detection and Localization of Car Damages Using Deep Learnin...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Parihar, Prachi Sachdeva, Manas Gupta, Deepak Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350369175

In the automotive industry, accurate and efficient vehicle damage detection is crucial for reducing the cost of claims and improving assessment procedures. This paper presents an automated method for vehicle damage detection using deep learning based You Only Look Once models, specifically YOLOv3, YOLOv5, and YOLOv8, in conjunction with two optimizers, AdamW and SGD. The COCOtoYOLO dataset contains pictures with six damage kinds. Gaussian Blur, Median Blur, Grayscale conversion, and Contrast Limited Adaptive Histogram Equalization (CLAHE) improved picture quality and feature visibility. The results reveal that YOLOv8, optimized with SGD over 200 epochs, achieved the highest mean Average Precision (mAP-50) of $72 \%$, surpassing other configurations. This study demonstrates the effectiveness of YOLOv8 and SGD in vehicle damage detection. The highest F1-score of ${9 3 \%}$ for glass shutter class claims the model's robustness for different types of car damages. © 2024 IEEE.

关键词： Automotive industry

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An Innovative Ensemble Approach to Detecting Distributed Denial of Service Attacks Using Machine Learning

An Innovative Ensemble Approach to Detecting Distributed Den...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Rajput, Deepak Singh Upadhyay, Arvind Kumar Amity University Dept. of Computer Science and Engineering Madhya Pradesh Gwalior India

ISBN: (纸本)9798331517953

In the present era marked by rapid Internet development, there is a growing emphasis on information security. Distributed Denial of Service (DDoS) attacks poses significant threats to servers globally. Various methods and services have emerged to prevent and mitigate damage caused by these attacks. The challenge in implementing defence lies in timely attack detection, requiring a highly accurate mechanism. DDoS attack methods are diverse and evolving, rendering traditional prevention methods less effective. This research explores the application of machine learning, utilizing the CICDDoS2019 dataset to train hybrid models. The proposed ensemble model combines unsupervised K-Means for dataset labelling and supervised classifiers (Random Forest, XGBoost, AdaBoost, SVM) with Artificial Neural Network (ANN). Achieving an overall accuracy of 99.46%, this study underscores the effectiveness of deep learning in DDoS attack detection. © 2024 IEEE.

关键词： Random forests

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Eva: Python-based Desktop Virtual Assistant for Visually Impaired 7

Eva: Python-based Desktop Virtual Assistant for Visually Imp...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Abraham, Anishamol Mathew, Benita Susan Mathew, Dona Lisa Mohammad, Fiaz S Krishnan, Gokul Amal Jyothi College of Engineering Dept. of Computer Science Kottayam686518 India

ISBN: (纸本)9798350372816

Virtual assistants have become essential resources in the digital age, providing users with a wide range of functionalities to expedite work and increase productivity. In this research, we provide a complete examination of Eva, a virtual assistant created using Python, and compare it to other virtual assistants such as Cortana and Copilot. Through a meticulous examination of Eva's features, we demonstrate its superiority in versatility and effectiveness. By embracing Eva's strengths and capitalizing on its competitive advantages, we push for a future in which digital experiences are inclusive and empowering for all users. © 2024 IEEE.

关键词： Python

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-dept. analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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