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Predictive Modeling of Fire Hazard: Integrating Environmental Factors with Machine Learning

Predictive Modeling of Fire Hazard: Integrating Environmenta...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Prediction of Parkinson's Disease using Hybrid Feature Selection based Techniques 4

Prediction of Parkinson's Disease using Hybrid Feature Selec...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Dash, Avijit Kumar Indian Institute of Technology Bombay Dept. of Computer Science and Engineering Powai India

ISBN: (纸本)9781665414470

Parkinson's disease (PD) is one of the significant severe problems globally in recent times. It is a neurological disorder that progresses over time and the most severe problems after Alzheimer's disease. Our article proposes a Hybrid Feature Selection system for the initial detection of PD from speech recordings. This method picks the best set of instances that can lessen instance vector dimensions from 22 to 5. We have proposed a machine learning-based model using five different classifiers named Random Forest, Logistic Regression, XGBoost, AdaBoost, and Gradient Tree Boosting. Gradient Tree Boosting presents the best appearance with a spectacular accuracy of 98.31% and the area under the ROC curve 98.66%, among all classifiers used to predict PD. We showed that the stated design has greater accuracy than the current methods available in the literature, and the number of instances is less than others. © 2021 IEEE.

关键词： Forecasting

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A Dual-Action Approach to Mosquito Control: Harnessing Mosquitoes Affinity for Light with Metofluthrin’s Lethality

A Dual-Action Approach to Mosquito Control: Harnessing Mosqu...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Manisha Mittal Mahender Singh Prachi Dewan Prabhmeet Singh Dept. of Electronics and Communication Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India Dept. of Computer Science & Engineering Guru Tegh Bahadur Institute of Technology (GGSIPU) New Delhi India

Amidst rapid urbanization and fluctuating climatic patterns, the world witnessed a surge in mosquito-borne diseases, escalating global health concerns. Mosquitoes, often dismissed as mere irritants, are carriers for a spectrum of conditions, including malaria, dengue, and the Zika virus, with WHO reporting large numbers of dengue cases annually. This review delves deep into the observation of mosquito behavior and the challenges in eradicating these pests, emphasizing the inefficacy of singular sensory targeting repellents. This paper introduces a pioneering device, combining the mosquito’s affinity towards light with the suffocating effects of metofluthrin, offering a holistic solution. By the combination of advanced engineering with a comprehensive understanding of mosquito behavioral dynamics, this device promises not only to repel but to substantially reduce these vectors, fostering safer indoor spaces. Through a detailed exploration of current methodologies and the introduction of this integrated strategy, this paper provides insights into creating a healthier, mosquito-free environment.

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Enhancing Telecommunications Customer Retention: A Deep Learning Approach Using LSTM for Predictive Churn Analysis

Enhancing Telecommunications Customer Retention: A Deep Lear...

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Data Science and Network Security (ICDSNS), International Conference on

作者： M Ranjith Kumar Priyanga S Janani Srinivasan Anusha Veda Chatiyode Jerome Santiago Dharminder Chaudhary School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Science & Engg Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The telecommunications industry has a significant challenge in customer churn, especially for companies aiming for sustainable growth in a competitive market. To overcome this issue, we suggested a method utilizing Long Short-Term Memory (LSTM) networks, a deep learning strategy that has revolutionized predictive modeling. We used 4250 customer profiles, each containing 19 features including consumption data and plan options, to train and test the LSTM model. Data preprocessing included encoding categorical variables, normalizing numerical features, and eliminating irrelevant variables to maintain data integrity and authenticity. The LSTM model was fine-tuned to recognize significant patterns rather than simply memorizing data. We also incorporated an additional layer to assess diverse information types, which improved the model's ability to process sequential data effectively. The proposed model achieved a prediction accuracy of 95.3% in identifying potential customer churn, demonstrating its effectiveness in predicting when customers might leave their telecommunications provider. These results demonstrate how LSTM networks can reduce customer attrition in the telecom industry, making them a valuable tool for companies aiming to retain customers and maintain growth in a competitive market. The study's findings pave the way for innovative applications of deep learning techniques in customer retention strategies across diverse industries. This breakthrough has the potential to transform business approaches to customer relationship management and loyalty programs.

关键词： Industries Deep learning Accuracy Data preprocessing Companies Predictive models Data models Telecommunications Numerical models Long short term memory

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Social Distancing Inspection To Mitigate COVID-19 Using K-Nearest Neighbour 6

Social Distancing Inspection To Mitigate COVID-19 Using K-Ne...

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6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Mishra, Rutuparnna Ransingh, Anshit Chakravarty, Sujata Dash, Satyabrata Centurion University Of Technology Management Dept. Of Computer Science And Engineering Odisha India

ISBN: (纸本)9781665425520

The beauty of Indian culture is to celebrate happiness as well as sorrows in gatherings of relatives, friends, and well-wishers. But with the advent of the COVID-19 pandemic, the world order has changed and social distancing has become inevitable and needs to be observed strictly to save mankind. Keeping in view the transmission of the COVID-19 virus through contacts and non-availability of medicines and vaccines, social distancing is one of the key solutions that is way out to contain this spread of the virus. As the name states, social distancing suggests that people should physically maintain distance between themselves which will reduce the chance of close contact and automatically leads to a decrease in the spread of infectious diseases like the Corona Virus and many other viruses which spread through close contact. Social distancing is possibly the most effective non-pharmaceutical way to prevent the spread of disease. The main objective of social distancing is if people are not close together, they are incapable of spreading germs. This mitigation measure is used to decrease transmission, by suspending and decreasing the size of the epidemic peak and spreading cases over a lengthier period to relieve the burden on the healthcare organization. In this paper, a model is recommended where the total number of people present in the frame is detected using the YOLO object detection algorithm, and distance between each individual is measured Using K-Nearest Neighbour. If the distance between any two individuals is less than 6 feet or 2 meters then a red bounding box pops around them indicating that they are violating the rule of social distancing. This model is implemented on Raspberry Pi with buzzer system for alert. © 2021 IEEE.

关键词： COVID-19

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Improved Performance Two-Dimensional Direction of Arrival Estimation Algorithm with Unknown Number of Noncoherent Sources 14

Improved Performance Two-Dimensional Direction of Arrival Es...

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14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

作者： Tayem, Nizar Hussain, Ahmed A Moinuddeen, AbuMuhammad Radaydeh, Redha M. Alghazo, Jaafar M. Texas A & M Univ. Commerce Dept. of Engineering & Technology United States Prince Mohammad Univ. Dept. of Electrical Engineering Saudi Arabia Texas A & M Univ. Commerce Dept. of Computer Science United States Virginia Military Institute Electrical & Computer Engg. Dept. United States

ISBN: (纸本)9781665487719

Two-dimensional direction of arrival estimation is a computationally complex problem that has been the focus of research in the area of array signal processing for several decades now. This paper proposes a novel2D DOA azimuth and elevation angle estimation algorithm for multiple RF noncoherent sources using an L-shaped array antenna configuration. The proposed method employs a MUSIC-like estimation algorithm in conjunction with QR decomposition to improve the accuracy of 2D DOA estimation and provide much lower computational complexity when compared with existing methods such as the Capon method. It does not require knowing the number of sources in advance and pairs the estimated azimuth and elevation angles automatically for multiple sources. Simulation results are presented to validate the efficacy of the proposed method and its performance is compared with the Capon method. © 2022 IEEE.

关键词： Computational complexity

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Terahertz Communications and Sensing Overview 3

Terahertz Communications and Sensing Overview

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Gupta, Richa Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India

ISBN: (纸本)9798350330724

Terahertz (THz) (0.1-10 THz) wireless communication is one of the cornerstones of the next 6G wireless networks. THz frequencies have the ability to dramatically increase wireless capacity performance and enable high-resolution environment sensing if applied properly due to the enormous quantity of accessible bandwidth. However, the usage of wireless devices in high-frequency bands like THz is constrained by a very unpredictable and dynamic channel. The ultimate result is essentially unreliable intermittent connections since these channel constraints have a naturally restricted communication range and a high susceptibility to blocking and chemical absorption. Because of this, the THz band's potential for high-rate communications and high-resolution sensing may be hindered. This study thoroughly examines the steps necessary to build up and operate next-generation THz wireless networks that will work together to deliver a variety of communication and sensing services in this environment. We first lay the groundwork for this by defining the THz frequency range's fundamentals. Using these fundamentals as a foundation, we outline and carefully investigate seven specific qualities that characterize THz wireless systems: Some of the subjects discussed include the quasi-opticality of the band, wireless architectures suited for THz, synergy with lower frequency bands, cooperative sensing and communication systems, PHY-layer protocols, spectrum access techniques, and real-time network optimization. These seven distinctive features enable our understanding of how to re-engineer wireless systems as we know them today to fit THz bands and their specific settings. On the one hand, THz systems make use of its quasi-optimality and may turn any sensing opportunity into a communication problem, aiding in the development of a new breed of flexible wireless systems that can do many jobs beyond straightforward communications. THz systems can alternatively use intelligent surfaces, lower

关键词： Internet of Everything

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BlockWheels - A peer to peer ridesharing network 5

BlockWheels - A peer to peer ridesharing network

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Joseph, Richard Sah, Rishabh Date, Atharva Rane, Pratik Chugh, Amay Vivekanand Education Society's Institute of Technology Dept of Computer Engineering Mumbai India

ISBN: (纸本)9781665412728

Blockchain is a technology that is essentially defined as a decentralized storage system for transactions, or ledger of all transactions or digital events that have been shared among the participating groups. A ridesharing system helps riders to reach a particular destination driven by the owner of the ride. There are many existing ridesharing services available in the market today. But most of these services are centralized and hosted by a third party which gives them the authority to monitor features like fare calculation, user data, etc. Privacy and trust are major concerns in such a system. The purpose of our paper is to make a decentralized application for ride-sharing where all transactions, fare calculation, matching and information is stored on a Distributed Ledger. The ledger will be accessible to all the peers in the network. This will match users with rides in a decentralized way without relying on third parties of any centralized network which makes the system transparent and reliable. The data on blockchain is immutable. Hence, this technology is used to store rides and user information to maintain security and ensure user privacy. © 2021 IEEE.

关键词： Smart contract

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Exploring the Potential: ML vs. DL in Network Security with Explainable AI (XAI) Insights

Exploring the Potential: ML vs. DL in Network Security with ...

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International Conference on computer and Information technology (ICCIT)

作者： Md. Tohidul Islam Md. Khalid Syfullah Jabbarul Islam H.M. Sazzad Quadir Md. Golam Rashed Dipankar Das Dept. of Information and Communication Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Electrical and Computer Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

The increasing complexity and diversity of cyber threats have driven the widespread adoption of Machine Learning (ML) and Deep Learning (DL) techniques for Network Intrusion Detection Systems (NIDSs) due to their capacity to effectively adapt and detect evolving threats. However, the inherent black-box nature of ML and DL-powered NIDSs has raised concerns surrounding explainability, transparency, and interpretability. This challenge has led to a prevailing sense of distrust in these systems. In this paper, we propose an improved Network Intrusion Detection System (NIDS) and introduce a ML and a DL-based classifier to detect malicious network traffics. To overcome the explainibility and trust issues, we innovatively integrate advanced Explainable Artificial Intelligence (XAI) techniques, such as SHAP, LIME, and ELI5, with our proposed ML and DL classifiers. These techniques provide vital insights and detailed explanations behind the decision-making processes of the NIDS. This approach is pivotal for multiple reasons, including ensuring that the classifiers are fair and unbiased, articulating the rationale behind their outcomes, or for debugging the models when they make mistakes. Furthermore, we evaluate and compare the performance of ML and DL classifiers using a variety of metrics such as Accuracy, Precision, Recall, F1-score, ROC AUC curve, PR AUC, Training time, Confusion matrix, and also compare the explainability the classifiers. The need to compare ML and DL-based classifiers stems from the aim to optimize network security measures, improve threat detection, ensure that resources are used efficiently in the face of evolving cyber threats, and understanding the differences in their decision-making processes. In this study, the ML Classifier (Accuracy 94.5%) outperformed the DL Classifier (Accuracy 93.5%).

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Cartoonize Images using TinyML Strategies with Transfer Learning

Cartoonize Images using TinyML Strategies with Transfer Lear...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Acharjee, Jashaswimalya Deb, Suman National Institute of Technology Agartala Dept. of Computer Science and Engineering Agartala India

ISBN: (纸本)9781665440325

The gradual advancements towards the development of Deep Neural Networks has ultimately led to the development of certain specialised hardware for the application of such Networks with superior performance. But, many of the consumer grade hardwares and Single Board computers (SBCs) which are used in embedded scenarios are not (yet) computationally efficient enough to perform the execution of even an already trained model within feasible limits. Thus in this paper, we focus on the implication of Post Training Quantization (PTQ) strategies to mitigate the great computational demands arising due to evolution of complex models. As the title implies, the original model incorporates a Generative Adversarial Network to generate Cartoonized versions of provided Real-World input images. This model, in its original state, takes nearly twice as much time to process the output on a single threaded workload. Our Solution to the above stated issue involves Quantizing the pre-trained model from 32-bit Floating Point values to a minimum of 8-bit Integer Values with the addition of Transfer Learning in-general. The results from our testing show that PTQ allowed the model to be compressed to a smaller size as compared to the original novelty, making it ready to be deployed on resource-constrained environments. In addition to that, a significant increase in the Inference Engine's processing performance has also been observed on general purpose hardware. © 2021 IEEE.

关键词： Deep neural networks

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