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IR Sensor Based Accident Prevention System for Hilly Areas

IR Sensor Based Accident Prevention System for Hilly Areas

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Singh, Jay Datta, Priyanka Kumar, Nagendra Sharma, Kailash Saxena, Abhinav Gupta, Amit Ambikapathy, A. Gl Bajaj Institute of Technology & Management Eee Department Gr Noida India Jss Academy of Technical Education Dept. of Eee U.P. Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Galgotias College of Engineering and Technology Dept. of Eee Gr. Noida India

ISBN: (纸本)9798350323887

This paper provide overview of the model which describe how to minimize the road accident on the curve roads by proposing a model which uses IR sensors as sensing elements for the vehicles coming from either side of the road IR sensor is connected with Arduino Uno software to alert drivers about the vehicles coming from either side of the road also Author have used 16∗ 2 counter to count the vehicles at the turning points to enhance the accuracy at the mountain roads the Arduino sense the signal from the IR sensors which commands the buzzer to start alarming the driver along with RGB LED present on either side of the road we have also use the LDR sensors for a street lighting system at the curve point which get activated at night automatically, in this paper for the energy-generating process we have used piezoelectric sensors in the speed breakers on both the sides through which energy is stored and utilized. © 2023 IEEE.

关键词： Microcontrollers

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A Hybrid Feature-Selection Method Based on mRMR and Binary Peacock Optimization Algorithm

A Hybrid Feature-Selection Method Based on mRMR and Binary P...

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International Conference on Computational and Characterization Techniques in Engineering & sciences (CCTES)

作者： Bibhuprasad Sahu Amrutanshu Panigrahi Abhilash Pati Rajalaxmi Mishra Sudhir Kumar Senapati Kashyapa Kanti Sahu Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of CSE Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha India Department of CSA College of IT and Management Education Bhubaneswar Odisha India Department of Computer Science Pj College of Management and Technology Bhubaneswar Odisha India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

An essential aspect of developing effective biomarkers that facilitate disease diagnosis and drug discovery is identifying the most critical characteristics from microarray data. Since it is expensive to validate biomarkers, the number of biomarkers must be reduced while retaining their effectiveness. A common challenge with microarray data is that it results in a large number of properties, known as the curse of dimensionality. Almost all the feature selection (FS) techniques developed up-to-date do not possess the ability to result in lower dimensionality, ensure sound classification, easy algorithms and exhaustive search of the parameter space. This study describes a hybrid FS approach by combining the mRMR and binary variant of the peacock algorithm (Binary POA) algorithm. It introduces mRMR by modifying its quantization functions to the continuous nature of the microarray data attribute and uses mRMR as a first step to normalize both datasets and select an initial feature subset. In the following step, a binary POA algorithm is deployed to optimize the selected feature subset. The performance of the proposed mRMR-BPOA ML model is evaluated using benchmark classifier Support Vector Machine. The Result analysis proves that the performance of mRMR-BPOA is superior to the existing counterparts.

关键词： Support vector machines Quantization (signal) Biomarkers Filtering algorithms Feature extraction Classification algorithms Drug discovery Medical diagnosis Optimization Cancer

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Prompt-Based Contrastive Learning to Combat the COVID-19 Infodemic

Prompt-Based Contrastive Learning to Combat the COVID-19 Inf...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Zifan Peng Mingchen Li Yue Wang Daniel Y. Mo Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong SAR China Khoury College of Computer Sciences Northeastern University Boston USA Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China Department of Supply Chain and Information Management The Hang Seng University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

The COVID-19 pandemic has triggered a surge in misinformation and disinformation online, particularly on social media platforms. The World Health Organization has highlighted the urgent need to combat this infodemic, as false information can lead to the spread of conspiracy theories, false remedies, and xenophobia. Efforts to combat the COVID-19 infodemic have faced challenges, with existing research often oversimplifying the issue by focusing solely on verifying the reliability of information. To effectively address this complex problem, a multifaceted approach is necessary. This study aims to broaden the perspective by analyzing texts from diverse angles, considering societal implications and the necessity of government intervention. We employ a prompt-based contrastive learning framework to meet the challenges. A prompt is a passage of text or query fed into a pretrained language model (PLM) to elicit a response. Prompts provide clear guidance and precise context for language models, which has been shown to make them more effective with limited training samples. This approach can help mitigate class imbalance and data sparsity issues in combating COVID-19 infodemics. We also incorporate prompts into a contrastive learning framework to better understand complex utterances within short, informal, unstructured social media texts. Contrastive learning is effective at distinguishing between useful and irrelevant input samples and focusing on the most discriminative features. Our research demonstrates prompt-based contrastive learning can reinforce each other and provide more accurate assessments of input text reliability than current baseline techniques. The framework not only addresses the challenges of data scarcity and class imbalance but also shows potential for application in other text classification tasks, particularly in low-resource languages with extreme class imbalance issues. In conclusion, the prompt-based contrastive learning approach presented in this s

关键词： COVID-19 Training Accuracy Social networking (online) Pandemics Text categorization Focusing Contrastive learning Fake news Surges

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Application of deep reinforcement learning techniques to optimise resource allocation in wireless communication Systems

Application of deep reinforcement learning techniques to opt...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Ravindra Bhardwaj B. Sashi Kanth Rakesh Kumar Joon Navyata Khadri Syed Faizz Ahmad G. Dineshnath Department of Physics and Computer science Dayalbagh Educational Institute (Deemed to be University) Agra Uttar Pradesh India Department of Electronics and Communication Engineering Vignan’s Institute of Engineering for Women (A) Visakhapatnam Andhra Pradesh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Kablana Jhajjar Haryana India Department of CDC SRM Institute of Science and Technology Delhi-NCR Campus Ghaziabad Uttar Pradesh India Department of Computer Science SRM University Amaravati Andhra Pradesh India Department of Computer Science Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Deep reinforcement learning (DRL) techniques are applied in wireless communication systems to ascertain the optimal resource allocation strategy for maintaining optimal system performance. The conventional ways of doing things about cellular networks are not working well enough to meet the growing demand for efficiently using the available scarce resources. This is due, in part, to the demand growing at an unsettling rate. The authors hope that this will lead to the creation of a fresh method for exploiting DRL’s capabilities to allocate resources in real-timeby network performance. Stated differently, that is the purpose of this. Deep neural networks are also a part of the system’s architecture. This allows the system to learn how to improve and adjust resource allocation strategies, which eventually leads to the system operating more efficiently as a whole. This is now being done to ascertain whether or not DRL is useful in addressing the difficulties related to wireless contact in addition to being possible. Only a few of the extra variables that are taken into account are changes in the volume of traffic, the state of the route, and user requirements.

关键词： Wireless communication Cellular networks System performance computer architecture Artificial neural networks Deep reinforcement learning Resource management

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Enhanced Feedback Residual Attention Network with Slanted Scarce Auto-Encoder and IoT Based Industrial Waste Water Treatment System for Effective Waste management

Enhanced Feedback Residual Attention Network with Slanted Sc...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prasad, K.V.S. Gavaskar, T. Ravivarman, G. Chinniah, P. Sophia, S. Sathyakala, S. Gmr Institute of Technology Department of Basic Sciences and Humanities Andhra Pradesh Vizianagaram India St. Joseph's College of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India ST. Joseph College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sona College of Technology Department of Management Studies Tamil Nadu Salem India

ISBN: (纸本)9798331509828

In the last few decades, rapid industrialization has increased the problem of wastewater treatment and management because of the increasing complexity and toxicity of wastewater that is difficult to treat by any conventional approach. Commonly used waste water treatment systems are unable to treat a variety of pollutants because of deficiency of good classification and optimization processes which results in wastage of resources and harming the environment. Furthermore, existing systems are inflexible, and an ability to change the nature of treatment in real time is impossible, which is crucial for the timely and efficient delivery and adjustment of therapies. To overcome these challenges, there is a need for smart monitoring systems enabled by IoT and effective treatment processes utilizing machine learning for better waste management. This paper comes up with an IoT and Machine Learning based system for the treatment of industrial wastewater that uses an Enhanced Feedback Residual Attention Network with Slanted Scarce Autoencoder to achieve accuracy in pollutant classification and quantification. Fick's Law Optimization is incorporated for optimization which makes the treatment process more efficient by allowing the treatment to be applied only where necessary and in the right amounts. IoT sensors are also part of the system to ensure that the data is collected in real time and the waste is continuously monitored to allow quick reactions to changes in waste. The introduced approach attains higher accuracy as 99%. © 2025 IEEE.

关键词： Wastewater treatment

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Advanced prediction of Heart diseases using Artificial Neural Network and Genetic Algorithm 5

Advanced prediction of Heart diseases using Artificial Neura...

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5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Savarapu, Pradeep Raj Shankar, M. Itnal, Shilpa Madupu, Ram Kumar Basha, Cmak Zeelan Sreedevi, E. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram AP India Marri Laxman Reddy Institute of Technology and Management Department of Eee Hyderabad Telangana India

ISBN: (纸本)9781665403603

As per the statistics stated in the year 2019 by WHO (World Health Organization), about 70% of deaths among the people aged above 40 years is due to heart attack. Main reason behind this is lack of earlier prediction of heart attacks. In order to improve the country's health care sector, it is a basic necessary to predict the heart attacks earlier and prevent deaths among the individuals. Prediction of heart related issues is done with the help of ECG (Electrocardiogram) data and also the clinical data. Here, the data is to be fed for non linear prediction of the disease. This monitoring module has to be made available for detecting atrial, myocardial, bradycardia, tachycardia, and fibrillation. An efficient method has been leveraged for acquiring clinical data and ECG data is proposed for training the neural network and accurately diagnose the functioning of heart and predict the abnormalities. The proposed work is divided into three steps. In the first step, the ECG of the patient is collected using the 3 led pre-jelled electrodes [2], [18]. ECG which is acquired is processed then filtered and amplified for removing noise. Ana log signal is then converted into the digital one. In the second step, heart rate(HR), FBS (fasting blood sugar), MAP(Mean Arterial Pressure), cholesterol(CH) along with gender is collected. Finally neural network is used for training and testing purpose. © 2021 IEEE.

关键词： Electrocardiograms

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A Dynamical Combination of Multiple Features-Based Approach for Classifying Agricultural Text 2

A Dynamical Combination of Multiple Features-Based Approach ...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： John, V.Y. Wasatkar, N.N. Yuvaraj, K. Bansal, Shubhansh Valarmathy, A.S. Goyal, Shitij School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai127 India Quantum University Research Center Quantum University Uttarakhand Roorkee247667 India

ISBN: (纸本)9798350388916

In the traditional text classification technique, values are read as letters, which destroys the original semantic illustration of numerical attributes. Completely mining deeper latent semantics features in agricultural literature requires a novel text classification technique that makes use of multivariate characteristic dynamic fusion. The global principal semantic components of the text were extracted using the Bi-Convolutional Neural Networks (CNNs) system using an attention mechanism, and the local semantic information was extracted using the Multiple Window Convolutional Neural Network. When classifying textual content based on phenotypic quantitative categories, the deep semantic portrayal of agricultural texts may be significantly enhanced by using the attention strategy to dynamically merge the obtained various semantic features. © 2024 IEEE.

关键词： Deep Learning Model Dynamic Combination Multiple Features Semantic Feature Extraction Text Classification

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Optimizing Congestion Control in Wireless Sensor Networks Using a Trust-Based, Security-Aware Approach 4

Optimizing Congestion Control in Wireless Sensor Networks Us...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Naveena, S. Sathiya Mishra, Abhinav Xavier, V.K. Rege, P.R. Anandhasilambarasan, D. Sharma, Divya Prince Shri Venkateshwara Padmavathy Engineering College Department of Mba Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798331518981

Both random events and self-centred nodes may trigger network congestion. In Wireless Sensor Networks (WSNs), bad nodes may cause congestion-like conditions by discarding data packets or transmitting duplicate, fraudulent packets. So, congested mitigation strategies alone won't guarantee level delivery. Consequently, isolating these points from the rest of the network is an essential first step in preventing this kind of trouble. Current work on congestion management solutions relies on the unrealistic and mistaken premise that every node is genuine and behaves correctly. To address this, researchers have developed a Security-Aware Congestion Control (SACC) algorithm, which accounts for both the usual and unexpected causes of congestion and how malicious nodes might prevent packets from receiving their intended recipients. These networks parameters - nodes' spare energy, reservoir utilization, and delivery of packets ratio - have been chosen for the purpose of establishing trust indicators because of the critical roles they play in detecting malicious behaviour and avoiding overcrowding. The suggested method outperforms similar current systems in energy efficiency, throughput management, and security, all thanks to its robust ability to resist several internal assaults. The suggested technique, Security-Aware Congestion Control (SACC), demonstrates its remarkable efficacy in guaranteeing safe and effective data transfer in wireless sensor networks with an astounding 96% accuracy rate. SACC performs better than other models like Low-Energy Adaptive Clustering Hierarchy (LEACH), Trust-based Congestion Control and Energy Efficiency Routing (TCEER), Secure Quality of Experience Energy Efficient Routing (SQEER) indicating its capacity to manage congestion and lessen the effect of rogue nodes, providing a more dependable and secure solution. © 2024 IEEE.

关键词： Sensor nodes

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Power Saving and Power Generating in Automobile (Self-Charging Kit)

Power Saving and Power Generating in Automobile (Self-Chargi...

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2023 International Conference on Disruptive technologies, ICDT 2023

作者： Singh, Jay Datta, Priyanka Kumar, Nagendra Sharma, Kailash Saxena, Abhinav Verma, Aditya Ambika Pathy, A. Gl Bajaj Institute of Technology & Management Eee Department Gr Noida India Jss Academy of Technical Education Dept. of Eee U.P. Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Galgotias College of Engineering and Technology Dept. of Eee Gr. Noida India

ISBN: (纸本)9798350323887

Nowadays most well Grounded and Slashed source of Energy is wind energy and it is easily within reach, convenient and easy to employ. So, as we are growing day by day with Technology, it's our responsibility to protect our nature for these Engineers and scientist are unfailingly working on different technologies to make human Life reliable and easy to access energy. So, we are also giving a little contribution to our society to make environment pollution free and help for humanities. This research paper is tiny contributions to our society in this confounding field so authors trust that in future this wondrous innovation could assist us with vanquishing all Reliable energy issues so that forthcoming Age could utilize that innovation. So here authors come with innovative solution Power Saving and Power Generating in Automobile using Wind Energy. © 2023 IEEE.

关键词： Wind power

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Enhancing Diagnostic Accuracy and Early Detection Through the Application of Deep Learning Techniques to the Segmentation of Colon Cancer in Histopathological Images

Enhancing Diagnostic Accuracy and Early Detection Through th...

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

作者： Mahaveerakannan R Shailee Lohmor Choudhary Rinku Sharma Dixit Srikanth Mylapalli M. Sathish Kumar Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur A. P Department of Information Technology Nehru Institute of Engineering and Technology Coimbatore Tamil Nadu

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Youth and elderly deaths are rising due to cancer. If it is not discovered early in cancer cell development, mortality will increase since it is deadly. Colon cancer arises when colon or rectum cells grow abnormally. Early colorectal cancer screening can prevent these abnormal growths from becoming cancerous. In health care systems, colon cancer diagnosis and categorization are crucial. Histopathology slide images are used to train a deep learning algorithm to detect colon cancer. The suggested model uses Vision Transformer (ViT) and Swin Transformer, a new type of ViT. Using an upgraded Swin Transformer model, the model accurately classifies tumours as benign or malignant. Effectiveness of colorectal diagnostics and precision cancer due to optimization concerns Their upgraded CNN models, ResNet34 and EfficientNet34, may not be enough to prevent optimization concerns from lowering colorectal cancer classification accuracy and efficiency. ResNet-101, Swin Transformer, ViT, and a revised Swin Transformer are also compared in this study. The proposed method uses LC25000 for training and testing. The test model has 99.80% precision using the modified Swin Transformer technique, 99.64% using the Swin Transformer itself, 99.36% using the ViT model, and 98.27% using ResNet-101.

关键词： Training Deep learning Accuracy Computational modeling Colorectal cancer Transformers Public healthcare Optimization Testing Tumors

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