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2nd International Conference on Sustainable Computing and data Communication Systems, ICSCDS 2023

作者： Durai, S. Iqbal, M. Mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVID-19

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Navigating the Landscape of Intelligent Systems, Smart Technologies, and User-Centric Evolution

Navigating the Landscape of Intelligent Systems, Smart Techn...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Sanjay Reddy Komatireddy Y. Krishna Bhargavi Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

Human-computer Interaction (HCI) is a dynamic field pi votal in shaping human-technology relationships. HCI is a multidisciplinary field, essential in understanding and enhancing the interaction between humans and technology. Challenges within HCI encompass design, usability, accessibility, and emotional resonance. The aim of this research study is to highlight the multifaceted nature of HCI, offering insights into its transformative and complementing potential and its continuous influence on the digital landscape. By delving into the challenges faced and the current technologies (Multimodal Affect Recognition, Artificial Intelligence, etc.), this review serves as a foundation for further research and development in this vital domain, offering valuable insights into the dynamic HCI landscape.

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EcoAI Forecast: Multi - Model Air Quality Prediction in Patancheru

EcoAI Forecast: Multi - Model Air Quality Prediction in Pata...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Karthikeyan, I. Jenefa, A. Edward Naveen, V. Santhiya, P. Sangeetha, R. Lincy, A. School of computer science and tech. Karunya Institute of Tech. and Sci. Coimbatore India Sri Shakthi Institute of Engg. and Tech. Dept. of CSE Coimbatore India National Engineering College Dept. of CSE Kovilpatti India

ISBN: (纸本)9798350354539

In the face of escalating air pollution in Patancheru, Hyderabad, the 'EcoAI Forecast' project presents a pioneering approach. It transcends the limitations of traditional linear models, which fail to effectively map the intricacies of air quality fluctuations. This research introduces a comprehensive multi-model machine learning framework, integrating sophisticated algorithms like LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network), and an Ensemble model. These models are adept.at processing complex environmental datasets, particularly focusing on the nuanced prediction of PM2.5 levels, a crucial air pollutant. Utilizing a dataset enriched with hourly PM2.5 measurements, the study illustrates a paradigm shift from conventional methodologies to more dynamic, data-driven techniques. The Ensemble model, a centerpiece of this research, demonstrates exceptional performance, achieving an RMSE (Root Mean Square Error) of 0.068 and an R2 (Coefficient of Determination) of 0.945. These metrics not only signify a substantial improvement over traditional models but also underscore the Ensemble model's precision in forecasting air quality. The success of the 'EcoAI Forecast' project paves the way for future advancements in environmental monitoring, exemplifying the transformative potential of advanced machine learning techniques in urban air quality prediction and public health preservation. © 2024 IEEE.

关键词： Air quality

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Prediction and Modelling of Suicidal Mental Tendencies

Prediction and Modelling of Suicidal Mental Tendencies

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Sunitha Guruprasad Arnav G Rao Shreeraksha Shivani K Varsha S N Yashoda K Dept. of Computer Science & Engineering St Joseph Engineering College Mangaluru India Visvesvaraya Technological University Belagavi Karnataka India Dept. of Computer Science & Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Suicide remains a pressing public health concern worldwide, necessitating effective predictive and preventive strategies. Predicting suicidal tendencies is a complex endeavor, as it involves multifaceted interactions between biological, psychological, and environmental factors. Advances in computational modeling and machine learning have provided promising avenues for understanding and forecasting these tendencies. Such models often integrate clinical data, including psychiatric history, symptom severity, and demographic information, with biological markers and psychosocial variables. The objective of our work is to identify patterns and risk factors that may indicate heightened suicidal risk. Ongoing research seeks to refine predictive models, incorporate real-time monitoring, and develop targeted interventions, aiming to reduce the incidence of suicide and provide timely support to those at risk. The application of suicide text analysis extends to user-friendly interfaces, allowing individuals to input text, receive predictions, and access relevant support. The analysis involves preprocessing techniques such as text normalization, stop-word removal, and stemming to enhance the quality of input data. Various machine learning algorithms, ranging from Naive Bayes to sophisticated ensemble methods, are explored to determine their effectiveness in predicting suicidal ideation.

关键词： Text analysis Computational modeling Soft sensors Predictive models Very large scale integration Natural language processing Real-time systems Bayes methods Wearable devices Robots

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An Efficient Analysis of Various Deep learning Model in Brain tumor Detection with MRI

An Efficient Analysis of Various Deep learning Model in Brai...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper compares three of the popular deep learning models being VGG-16, VGG-19 and ResNet50 with respect to how they perform in the classification task. We investigate how much each model learns, the reduction of errors and inferences from data over epochs, through an in-dept. analysis of training and testing phases. The authors concluded that all the models perform efficiently; however, from the study results of performance and accuracy, VGG-19 and ResNet50 have a high-performance scale as well as super accuracy in their deep learning model which encourages business for application towards complex pattern recognition or predictive modelling. These results provide important intuitions on what deep learning architectures can do, and pave the path for further works in this area, b

关键词： Deep learning Training Analytical models Accuracy Computational modeling Predictive models Brain modeling data models Residual neural networks Testing

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Enhancing Industrial Load Scheduling through Demand Side Management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep Learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

This study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. The pressure, humidity, and temperature may all change as a result of changing the motor's output current. The results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. The results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. The research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Developing a Face Aging Model Using Face Synthesis Techniques

Developing a Face Aging Model Using Face Synthesis Technique...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Priyanka Kaushik Zakiya Manzoor Khan Muntaha Manzoor Khan Beenu Mago K. Jeevanadan Shivansh Dwivedi Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science Lovely Professional University Punjab India Dept. of Computer Science NIILM University Haryana India Dept. of Computer Science Skyline University College Sharjah UAE Dept. of Civil Engineering Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Regarding the realm of advancing technology, the subsequent development of face aging applications has garnered substantial interest. This research examines face synthesis techniques, with specific emphasis for the various practical usage of computational adversarial networks (GANs). The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age-progressed face images. The project assimilates Python programming, convolutional neural networks (CNNs), and several other machine learning and image processing proceeds, advancing in qualitative data from Kaggle. The goal of our project is to advancement of face-aging technology over the use of this multifaceted method, with the possible uses in amusement, forensic science, and technological process.

关键词： Ethics Technological innovation Forensics Aging Generative adversarial networks Generators Convolutional neural networks Faces Stress Python

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A Semi-Supervised Approach For Detecting Agricultural Land Reduction In Bangladesh Using Satellite Images

A Semi-Supervised Approach For Detecting Agricultural Land R...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Wahid Mahmood Shafin Sk. Md. Masudul Ahsan Dept. of Computer Science and Engineering Khulna University of Engineering & Technology (KUET) Khulna Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Recently, remote sensing change detection has significantly improved because of greater access to global satellite images and developments in satellite technology. Several CNN-based techniques have demonstrated exceptional precision. Detecting the amount of agricultural area that has been reduced is a type of change detection problem that can easily be solved using deep learning methods on satellite data. But the majority of deep learning methods have been specifically created for high-resolution images. This data lacks global coverage, consists of at most 3 to 4 bands, and is costly. The Sentinel-2 satellite, which was launched in 2015, offers medium-resolution multi-spectral image data consisting of 14 bands and provides free global coverage. Having more bands enhances the capabilities for model analysis. But there is a lack of labeled satellite data. This research proposes a semi-supervised method for generating ground truth by employing a multi-step procedure. This technique began with semi-supervised classification to label the images, followed by a sequence of refinement processes to enhance the accuracy of the ground truth mask. The mask was created using the XGBoost machine learning technique. The dataset was assessed using the output images generated by the trained model. Detecting changes in low-resolution image may be difficult, but proposed method will greatly streamline the procedure. Applying this approach revealed an approximately 8.35% decrease in agricultural land area inside the test image area between 2018 and 2022. In the test area of 237.77 square kilometers, the reduced area measures approximately 19.861 square kilometers.

关键词： Deep learning Deforestation Satellites Accuracy Computational modeling Urban areas Area measurement Streaming media Satellite images Remote sensing

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Advancements in User Security: Enhancing Usability with Graphical Password Authentication

Advancements in User Security: Enhancing Usability with Grap...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Harisha Sandhya Ramesh Naik Shettigar Sarvani Vasudeva K Shrilakshmi Vaishnavi Kothwal Dept. of Computer Science & Engineering Sahyadri College of Engineering & Management Mangalore Karnataka India

Text-based password authentication is a common method used to verify the identity of users who are trying to access a secure system or service. In order to use this authentication method, the user must input a password or other secret phrase that is then compared to a server-side copy of the same password. Access is given if the password typed matches the one saved. Graphical password authentication is a type of user authentication that involves using images or visual elements instead of alphanumeric characters to verify the identity of the user. Unlike traditional text-based passwords, graphical passwords offer an intuitive and user-friendly way of authentication, as they rely on the user's ability to remember pictures, shapes, and patterns. This technology has been developed to address the limitations of traditional text-based passwords, such as the difficulty of creating and remembering complex passwords, and the vulnerability to brute-force attacks. Compared to conventional text-based passwords, graphical password authentication has a number of benefits, including better usability and higher security.

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Speeding Up EfficientNet: Selecting Update Blocks of Convolutional Neural Networks using Genetic Algorithm in Transfer Learning

arXiv

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arXiv 2023年

作者： Hasan, Md. Mehedi Ibrahim, Muhammad Ali, Md. Sawkat Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Computer Science and Engineering Univeristy of Dhaka Dhaka Bangladesh

The performance of convolutional neural networks (CNN) depends heavily on their architectures. Transfer learning performance of a CNN relies quite strongly on selection of its trainable layers. Selecting the most effective update layers for a certain target dataset often requires expert knowledge on CNN architecture which many practitioners do not posses. General users prefer to use an available architecture (e.g. GoogleNet, ResNet, EfficientNet etc.) that is developed by domain experts. With the ever-growing number of layers, it is increasingly becoming quite difficult and cumbersome to handpick the update layers. Therefore, in this paper we explore the application of genetic algorithm to mitigate this problem. The convolutional layers of popular pre-trained networks are often grouped into modules that constitute their building blocks. We devise a genetic algorithm to select blocks of layers for updating the parameters. By experimenting with EfficientNetB0 pre-trained on ImageNet and using Food-101, CIFAR-100 and MangoLeafBD as target datasets, we show that our algorithm yields similar or better results than the baseline in terms of accuracy, and requires lower training and evaluation time due to learning less number of parameters. We also devise a metric called block importance to measure each block’s efficacy as update block and analyze the importance of the blocks selected by our algorithm. © 2023, CC BY-NC-ND.

关键词： Genetic algorithms

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