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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Prakash Kunar Nithish R Sudha Department of Masters of Computer Applications Hindustan Institute of Technology and Science

In recent years, the process of industrialization and urbanization has accelerated the global economy, which benefits people but has serious environmental consequences. Air quality has become a major concern for both countries and individuals. We propose a very effective method for predicting PM2.5 levels in Chennai using Graph Convolutional Networks (GCN) and Bi-directional Long Short-term Memory (Bi-LSTM) models. As input features, we use historical PM2.5 data, wind speed, humidity, temperature and precipitation. To improve the model's robustness, data preprocessing methods such as Data Cleaning, Data, Data Transformation and Data Reduction was *** shifting the time-series data by a small time-interval, Randomly scaling the data by a small factor and Randomly applying noise to the data was done during data Augmentation. GCN works by propagating information between nodes in the graph, with each node's representation being updated based on the representations of its neighbors. In this case, the nodes would correspond to different locations in the city, and the edges would represent the spatial relationships between them. whereas the Bi-LSTM component captures the temporal relationships. The proposed method is evaluated on a real-world data-set and shows significant improvement in prediction accuracy compared to traditional methods. The performance of the model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE).

关键词： Measurement Temperature distribution Precipitation Biological system modeling Atmospheric modeling Wind speed Urban areas

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Machine Learning-Driven Economic Modeling for Enhanced Unemployment Rate Prediction and Analysis

Machine Learning-Driven Economic Modeling for Enhanced Unemp...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Rakhi Arora Nitin Dixit Muskan Sihare Shreshtha Kinger Sneha Shivhare Komal Kulshreshtha Computer Science and engineering Institute of technology & Management Gwalior India Masters of Computer Application Vikrant University Gwalior India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

This study presents a comprehensive analysis of economic modeling using Python, specifically focusing on the prediction of unemployment rates. In response to the growing significance of data-driven decision-making in economics, our research explores the application of logistic regression and random forest classification models to comprehend and forecast unemployment status. The dataset utilized in this analysis encompasses historical unemployment rate data, serving as a valuable source for our modeling endeavors. Our methodology begins with logistic regression, establishing a baseline model to scrutinize the relationship between diverse economic indicators and the binary classification of unemployment status (above or below 10%). We evaluate the model's efficacy in capturing trends over time and identifying influential factors in determining unemployment status. Subsequently, we advance our approach by employing random forest classification, a sophisticated machine learning technique adept at capturing non-linear relationships and interactions within the data. Our investigation shows that both random forest classification and logistic regression are good at predicting jobless status. We conduct a comparative evaluation of these models, shedding light on their respective strengths and limitations. Additionally, we delve into the significance of feature selection, hyperparameter tuning, and model evaluation within the realm of economic modeling using Python.

关键词： Logistic regression Analytical models Accuracy Biological system modeling Computational modeling Predictive models Market research Unemployment Random forests Tuning

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DeepDerm: Elevating Precision in Dermatological Diagnosis with Enhanced CNN 15

DeepDerm: Elevating Precision in Dermatological Diagnosis wi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mundada, Monica R. Seema, S. Sowmya J, B.J. Student, Sonia Fathima Ramaiah Institute of Technology Department of Masters of Compiuter Application Banglore India Ramaiah Institute of Technology Department of Computer Science and Engineering Banglore India Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Banglore India

ISBN: (纸本)9798350370249

Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings requires dermoscopic analysis and clinical screening due to medical criteria. It gets more difficult to classify skin diseases using medical photographs because of the complex forms, wide range of colors, and data security issues. In this study, we offer a dependable approach for the classification of 20 distinct types of skin illnesses, based on an improved Convolutional Neural Network (CNN) model. Our enhanced CNN design exceeds the standard CNN, which only achieves an accuracy of 31.38%, with an astounding accuracy of 98.21%. The accuracy of two benchmark algorithms, AlexNet and VGG19, is also not particularly high (32.14% and 52.30%, respectively). In contrast to traditional CNN models the enhanced CNN performs better, demonstrating its efficacy and dependability in accurately identifying a wide range of skin disorders. including well-known architectures like AlexNet and VGG19. Positive implications of this discovery include the development of more precise diagnostic tools for dermatology. © 2024 IEEE.

关键词： Benchmark Algorithms CNN Enhanced CNN Skin Disease Classification

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Healthy Eats - Online Food Delivery That Urge To Eat Healthy and Live More

Healthy Eats - Online Food Delivery That Urge To Eat Healthy...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Janupally Vedika Reddy Janupally Bindu Madhuri Reddy Computer Science and Engineering B V Raju Insitute of Technology Narsapur Medak India Masters in Computer Science Texas State University San Marcos TX USA

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

The demanding growth for the healthy food options and increasing organic food has led us, to the emergence of this new innovation healthy Eats website which is a perfect destination to urge eat healthy and live more ,Moving from greater Himalayas to Kanyakumari India has diversified historical cuisines and our culture is strongly bonded from years to organic food and farms which our ancestors has been cultivated , cook and served, while compared to other cuisine it is important to make informed choices in this website we will achieve the process to make healthy food access to each and every individual, In this website we provide a unique feature where the customer can do a Zoom call to the chief and monitor regarding the food freshness , customer preference’s and food hygiene as well as customer can also customise their food and can lively view and suggest their preference and order from their near by vicinity .After the post pandemic we come across enormous change in the food habits ,life style of people to overcome daily life style disorders, stress and lack of time we observe huge demand for the organic food ,Since 10 years due to heavy civilization and huge urbanization, people have shifted to urban areas, due to lack of time and of healthy organic food in their desired way will create a huge demand to this website. Through our website we encourage farmers to supply the organic products ensuring the profit to the both the parties .Our healthy Eats will be working as the interface for both farmers and customers ,Through our Healthy Eats website we will approach the farmers and then buy the organic products from the farmers and we check the farmers have the organic certification to ensure that their products meet the organic standards. We also consider components such as variations, cost of the production, and market needs. Transparency and Integrity are the main keys to maintain the long term partnerships. Our website will also ensure the profit-sharing model tha

关键词： Technological innovation Urban areas Employment Production Agriculture Real-time systems Standards

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An Innovative Novel Transformer Model and Datasets for Safeguarding Religious Sensitivities in Online Social Platforms

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Procedia computer science 2024年 233卷 988-997页

作者： Mohammad Shariful Islam Mohammad Abu Tareq Rony Mejbah Ahammad Shah Md Nazmul Alam Md Saifur Rahman Computer Science and Telecommunication Engineering Noakhali Science and Technology University Noakhali 3814 Bangladesh Department of Statistics Noakhali Science and Technology University Noakhali 3814 Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka 1229 Bangladesh Masters of Computer Science Arizona State University Tempe 85287 USA Ph.D. in Strategic Management University of the Cumberlands Williamsburg 6178 USA

Nowadays, blasphemy, inter-religious hatred, and intolerance toward other people's faith become increasing problems in different social media. To effectively tackle these problems, it is necessary to develop efficient techniques for automated detection. With the increasing concern about hate speech on the internet, this research study focuses on creating and testing advanced models to identify and categorize texts related to religious hatred. Leveraging two meticulously curated datasets, the first comprising binary classification tasks to discern hate speech directed towards religion and the second featuring a nuanced five-class categorization including Blasphemy, Ethnic Hatred, Ethnic Group Hatred, Undetermined, and Non-Hatred. The main focus of the research is examining the effectiveness of different established machine learning techniques, deep learning methods, and pre-trained transformer models. Notably, the study introduces hatebnBERT, a novel BERT-based model tailored to the Bangla language's specific nuances and linguistic intricacies specifically built for hate speech detection. Through rigorous evaluation and comparative analysis, the results demonstrate the superior accuracy and precision of hatebnBERT, achieving an accuracy of 98.8% in the binary-class dataset and 98.6% in the multiclass dataset, in effectively identifying and classifying various forms of hate speech within the realm of religious discourse. The outcomes underscore the significance of leveraging specialized language models, such as hatebnBERT, to combat the proliferation of abusive speech and promote a more courteous and inclusive online social media environment within the Bangla-speaking community, considering the potentially devastating consequences of unchecked religious hatred propagation. Insert here your abstract text.

关键词： Hate speech classification NLP Bangla text dataset Transformers Religion

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Multi Perspective Aerodynamic Studies on the Intercity Bus with and Without Drag Reductions Techniques by Using Engineering Approaches 48th

Multi Perspective Aerodynamic Studies on the Intercity Bus w...

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48th National Conference on Fluid Mechanics and Fluid Power, FMFP 2021

作者： Vijayanandh, R. Senthil Kumar, M. Padmanaban, Sujithira Syed Masood, T.S. Arul Prakash, R. Vijayakumar, M. Aeronautical Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore64049 India Masters in Computer Simulation in Science Bergische Universitaet Wuppertal Gaußstraße 20 Wuppertal42119 Germany

ISBN: (纸本)9789811969690

In Automotive vehicles, the effects of opposing aerodynamic force and side drifting force are one of the prime unstable factors. With the action of an unstable environment in the automotive vehicle, the possibility of accident occurrence is high. Hence the detailed research about the generation of opposing aerodynamic force and side drifting force effects and their handling techniques are mandatory for smooth and secure travel. In this work, an intercity bus is selected as a major object, in which Computational Fluid Dynamics (CFD) analysis played a major role in the estimation of side force on the bus. Also, experimental tests are conducted for validation purposes. Totally nine models are designed with the help of CATIA, in which the first model is a normal intercity bus and another eight models are equipped with drag reduction techniques such as dimples, riblets, fins on its upper surface. The comparative analyses of prediction of side force variations on the bus models are estimated with the help of ANSYS Fluent 16.2. Finally, dimples equipped at the top and side surfaces on the bus model [DESIGN–I] is suggested as a more efficient model than other models because Dimple is a key component, which can reduce the pressure drag on the bus. Also, the suggested model can provide comfortable travel by reducing the side force generation without affecting the preliminary requirements. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Adaptive Deep Reinforcement Learning-based Resource Management for Complex Decision Making in Industry Internet of Things Applications 4

Adaptive Deep Reinforcement Learning-based Resource Manageme...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Karne, Niharika Ramagundam, Chandra Shekar Patnala, Ranga Rao Ramagundam, Shashishekhar Tadiboina, Sai Nitisha Sr. Principal Systems Engineer in C&RS Platform Services University of South Alabama Department of Electrical Engineering Masters in Electrical Engineering Amtrak DT United States Software Engineer in Cloud Platform Services City of Philadelphia Pennsylvania United States Vice President Neuberger Berman United States Software Development & Engineering in Content Discovery & AI Troy University Department of Computer Science Masters in Computer Science AL Comcast Montgomery Campus United States Department of Information Technology GEICO - Chevy Chase Chevy ChaseMD20815 United States

An independent industrial system may transform into a connected network through the assistance of Industrial Internet of Things (IIoT). The deployed sensors in the IIoT maintain surveillance of the industrial machinery and equipment. As a result, safety and reliability emerge as the primary concerns in IIoT. This presents a variety of well-known and increasing issues related to the industrial system. The IIoT devices are exposed to a wide range of malware, threats, and assaults. To prevent the IIoT devices from malware effects, effective protection plans must be implemented. But adequate security mechanisms are not be incorporated in IIoT devices with limited resources. It is essential to ensure the accuracy and dependability of information gathered by IIoT devices. Decisions taken with incomplete or inaccurate data might be devastating. To overcome these difficulties deep learning with reinforcement learning for complex decision-making in industry applications is developed in this research work. In this developed model, an Adaptive Deep Reinforcement learning (ADRL)-based resource management is performed to reduce the operation cost associated with IIoT deployments. Energy efficiency is essential in IIoT ecosystem, particularly for the devices that run on batteries. Through dynamic resource allocation based on workload needs and energy limits, ADRL-based resource management optimizes the usage of energy. The reliability of the designed model is enhanced by fine-tuning the parameters from DRL using the Ship Rescue Optimization (SRO) algorithm. Thus, ADRL-based resource management systems make real-time decisions based on current environmental conditions and system requirements. This helps the IIoT systems to react quickly to change demands and optimize resource allocation. Finally, the experimental analysis is performed to find the success rate of the developed resource management system via various metrics. Throughout the validation, the statistical analysis of the

关键词： Deep reinforcement learning

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Eye Cataract Disease Classification: A Comparative Model Performance Analysis Under Data Constraints

Eye Cataract Disease Classification: A Comparative Model Per...

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International Conference on computer and Information Technology (ICCIT)

作者： Mahmudur Rahman K. M. Sadman Sakib KBM Tahmiduzzaman Md Anikur Rahman Jerin Akther Munia Abdul Hady Akash Computer Science and Engineering Daffodil International University Dhaka Bangladesh Electrical and Electronic Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh Masters of Information Technology University of Technology Sydney Sydney Australia Masters of Cyber Security Washington University Science and Technology Alexandria Virginia United States Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Blindness frequently arises from cataracts, which often require to be detected early for optimal treatment. Timely diagnosis is crucial since severe cases might require surgery. In such instances, computer-aided diagnosis can help the doctor detect the patient immediately by assisting in distinguishing the cataract condition from a normal eye. Finding enough cataract imaging data to train a custom Convolutional Neural Network(CNN) model can be challenging. Even with proper augmentation, a custom CNN model will still perform badly on testing data because it is unable to comprehend the extensive features offered by the enhanced data. However, with small as well as augmented data, the pre-trained model can still give better results than the custom CNN. Pre-trained models are trained to utilize augmented data to extract a wide range of features while boosting accuracy. Recall and accuracy are crucial factors in any disease screening process. In this study, we aimed to augment the data to mitigate the imbalance and scarcity of data, giving our pre-trained model more features to train efficiently. Later, to improve the model’s performance, we ensembled the models and assessed the ensemble models with optimal weights to find the best result on the test data, yielding an accuracy of 98.62% and Recall, Precision, F1 Score of 99% respectively.

关键词： Cataracts Analytical models Accuracy Computational modeling Feature extraction Data models Convolutional neural networks Ensemble learning Diseases Residual neural networks

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Facial Expression Recognition (FER) System Using Deep Learning

Facial Expression Recognition (FER) System Using Deep Learni...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Ooi Juin Yan Humaira Ashraf Uswa Ihsan Nz Jhanjhi Sayan Kumar Ray Kavita Masters of Applied Computing Taylor’s University Selangor Malaysia School of Computer Science SCS Taylor’s University Malaysia Department of Computer science International Islamic University Islamabad Pakistan Department of Computer Science and Engineering Uttaranchal University Dehradun India

Sentiment analysis of both text and images have been widely implemented machine learning application. Facial expression recognition (FER) is a widely known application of sentiment analysis of image data. This application has already been implemented in various industry, but challenges such as the complexities of facial features and different image angle still remain. This project aims to develop a FER system capable of classifying human expressions through facial images using deep learning techniques. A CNN-based architecture was proposed and training using the FER2013 dataset, which is a commonly used facial expression dataset. Overall, the trained model achieved an accuracy of 63.97% through the dataset. The trained CNN model was then employed to develop a web-based prototype of a FER system through Python Flask.

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Autonomous Robot for Disaster Mapping and Victim Localization

arXiv

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arXiv 2024年

作者： Potter, Michael Bhowal, Rahil Zhao, Richard Patel, Anuj Cheng, Jingming Cognitive Systems Lab Northeastern University Boston United States Masters in Robotics Northeastern University Boston United States Masters in Computer Science Northeastern University Boston United States

In response to the critical need for effective reconnaissance in disaster scenarios, this research article presents the design and implementation of a complete autonomous robot system using the Turtlebot3 Burger (TB3) with Robot Operating System (ROS) Noetic. Upon deployment in closed, initially unknown environments, the system aims to generate a comprehensive map and identify any present’victims’ using AprilTags as stand-ins. We discuss our solution for search and rescue missions, while additionally exploring more advanced algorithms to improve search and rescue functionalities. We introduce a Cubature Kalman Filter (CKF) to help reduce the Mean Squared Error (MSE) [m] for AprilTag localization and an information-theoretic exploration algorithm to expedite exploration in unknown environments. Just like turtles, our system takes it slow and steady, but when it’s time to save the day, it moves at ninja-like speed! Despite Donatello’s shell, he’s no slowpoke - he zips through obstacles with the agility of a teenage mutant ninja turtle. So, hang on tight to your shells and get ready for a whirlwind of reconnaissance! Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

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