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Development of an Air Pollution Monitoring System

Procedia computer science 2025年 260卷 645-654页

作者： Rekha Kaushik Eshan Kelkar Aryan Gupta Arpita Bhargava Deparment of Information Technology Indian Institute of Information Technology Bhopal 462003 India Deparment of Computer Science and Engineering Indian Institute of Information Technology Bhopal 462003 India

Clean air is a fundamental requirement for the proper functioning of the human body. Increasing population, urbanization and industrialization has led to a stark degradation in air quality. Bad air quality has severe effects on humans, wildlife, vegetation and man-made architectures. Hence, forecasting future air quality and monitoring current air quality is of utmost importance. This paper presents a comparative analysis of various machine learning techniques for air quality forecasting and an analysis of various sensors for air quality monitoring. In addition, a sensing architecture for monitoring air pollutant concentration on a wide scale has been delineated. This work would help in detecting the factors involved in degradation of air and plan measures to improve the air quality.

关键词： Air Quality Pollution Air Quality Index Time Series Forecasting Air Quality Monitoring Sensors IoT

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Contact Tracing over Uncertain Indoor Positioning Data (Extended Abstract) 40

Contact Tracing over Uncertain Indoor Positioning Data (Exte...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Liu, Tiantian Li, Huan Lu, Hua Cheema, Muhammad Aamir Chan, Harry Kai-Ho Aalborg University Department of Computer Science Denmark College of Computer Science and Technology Zhejiang University China Roskilde University Deparment of People and Technology Denmark Monash University Faculty of Information Technology Australia Information School University of Sheffield United Kingdom

ISBN: (纸本)9798350317152

Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object o, e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with o for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals. © 2024 IEEE.

关键词： Viruses

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Predicting Schizophrenia from fMRI Using Deep Learning 1

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3rd International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kardani, Shail Sharma, Raghav Sharma, Abhishek Deparment of Computer Science Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Deparment of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Schizophrenia is a complex and multifaceted disorder that is still not fully understood. However, many processes are known to drive its onset, relapse, symptomatology, and therapy. One possible explanation for Schizophrenia is that the person has difficulty using proper analytic methods to deal with the complexity and variety of the condition. Many AI algorithms have been designed and developed over time specifically for Neuroinformatics. These algorithms have been a go-to for many researchers and scientists, even for those in the medical field. Because of these AI-powered algorithms, Scientists are able to detect various diseases, whether they are related to brain activity or any other organs. Deep Learning is yet another field of Machine Learning which is made by the inspiration of how neural networks are connected inside our brain. These are more flexible than the Scikit-Learn-based Machine Learning models as these Deep Neural Network models don’t often require data preprocessing at initial stages. Not only this fact, but also they are used in various places, for example, processing of unstructured data, including texts and images, feature extraction, etc. In this study, we have attempted to detect Schizophrenia from resting-state functional magnetic resonance images using trained Deep Neural Networks as well as Scikit-Learn-based state-of-the-art Machine Learning models. For the dataset, we have utilized the public COBRE dataset available in the nilearn library, along with some public datasets from OpenfMRI. The Deep Learning models detected Schizophrenia with a reliable performance compared to Scikit-Learn-based Machine Learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance

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Recognition of Secure Data Transmission in Cloud Platform using Deep Learning 3

Recognition of Secure Data Transmission in Cloud Platform us...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Bhavya, R. Kumar, M. Guru Vimal Ramya, U.M. Janagi, R. Ganesan, M. Deepa, S. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. SangunthalaR&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Deparment of Information Technology Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Hindusthan College of Engineering and Technology Deparment of Information Technology Coimbatore India Kongu Engineering College Perundurai Department of Computer Technology-PG Erode638060 India

ISBN: (纸本)9798350375190

The recognition of secure data transmission in a cloud platform requires the integrity of sensitive information with higher confidentiality. This is obtained using the aid of deep learning techniques. The robustness of the system is improved using a random forest algorithm. In secure data transmission, the deep learning algorithms are trained to adopt various patterns of communication with the identification of certain anomalies. They result in security threats. They involve suspicious user behaviour with unexpected information exchange. The overall accuracy and interpretability of the system are achieved using a random forest algorithm which combines multiple decision trees. They are used for handling the high-dimensional data and obtaining the relationship between complex patterns. They helps in the identification of various security threats in the data communication through cloud platforms. This helps in the automatic decision-making process during malicious behaviour. The obtained dataset contains both the secure and insecure datasets. The extracted features acts as an input for the random forest classifier. These features are analyzed by the random forest and provides information that helps to influence the decision-making process. The integrated approach improves the accuracy of the security threats with enhancing transparency in the automatic decision-making techniques. They provide trust in the security measures in the cloud platforms. The collaboration of deep learning with a random forest algorithm helps in obtaining secure data transmission. © 2024 IEEE.

关键词： Behavioral research

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Development of Virtual Reality Platform through Human computer Interaction using Artificial Intelligence 3

Development of Virtual Reality Platform through Human Comput...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Saranya, S. Channarayapriya, B. Harshavardhini, U. Nandhini, A.Sunitha Revathi, J. Venkatesan, R. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Deparment of Information Technology Chennai India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Mathematics Erode India Bannari Amman Institute of Technology Deparment of Information Technology Erode India

ISBN: (纸本)9798350375190

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments. © 2024 IEEE.

关键词： Virtual reality

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

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Ethiopic Cryptography: An Efficient Hybrid Encryption Algorithm for Reducing Cybersecurity Attack 11th

Ethiopic Cryptography: An Efficient Hybrid Encryption Algori...

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11th EAI International Conference on Advancement of science and technology, ICAST 2023

作者： Akele, Tadesse Birara Bahiru, Tadesse Kebede Molla, Tibebu Legesse Fentie, Shambel Dessale Belay, Tadesse Destaw Marye, Almaz Yemataw Getaneh, Mersha Derese Department of Information System Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Information Technology Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Deparment of Software Engineering Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia Department of Computer Science Kombolcha Institute of Technology Wollo University Kombolcha Ethiopia

ISBN: (纸本)9783031641503

Cryptography is an essential tool in ensuring the security of data during network communication. Network and service providers prioritize data security to ensure secure data exchanges. Ethiopic cryptography is a unique encryption method that utilizes the Amharic language, prevalent in Ethiopia. Unlike Latin alphabets, Amharic consists of 287 characters, making decryption challenging. The encryption process involves converting the text to Amharic and then mapping it to a pair of randomly chosen two-bit English alphabets, creating an intermediate cipher. The Ethiopic cryptography algorithm employs a hybrid approach, using the RSA algorithm for secure key exchange and the Advanced Encryption Standard algorithm for text confidentiality. The algorithm comprises three phases—translation, mapping, and encryption—contributing to its effectiveness in securing data over the network. Decryption requires familiarity with the Amharic language and mapping details. The algorithm demonstrates a stronger avalanche effect (97%) in comparison with Blowfish and DES. The algorithm is proposed and evaluated using the design science research methodology, and its performance is validated through simulation using MATLAB and BunnaScript tools, considering factors like the avalanche effect, time complexity, and memory consumption. The evaluation shows that the suggested solution operates faster, requires lesser time for data encryption, and uses less memory than other methods in use. Overall, the proposed Ethiopic cryptography algorithm proves to be an efficient and effective method for securing data during network communication, offering stronger text confidentiality and an improved avalanche effect. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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Progression of Disease Forecasting in Recurrence Cervical Cancer Using Logistic Regression 7

Progression of Disease Forecasting in Recurrence Cervical Ca...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Geeitha, S. Renuka, P. Ramya, S. Sinduja, K. M.Kumarasamy College of Engineering Department of Information Technology Karur India Paavai Engineering College Deparment of Computer Science and Engineering Namakkal India Sri Krishna College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350340600

Cervical cancer is the fourth most prevalent disease in women globally as well as main cause of cancer- related mortality among women. The overall survival rates of cervical carcinoma patients are significantly impacted by the detection of recurrence. The disease is entirely treatable, though, if it is found in its early, precancerous levels. According to current figures, the death percentage a few decades ago was excessive. Women who have previously had treatment with CIN3 are most at riskof developing cervical cancer once more. The dataset includes information on the effects of various treatments on post- recurrence survival. The progression of a disease is detected in this research. Of the 4913 patients who were diagnosed withcervical cancer overall, 260 women with the disease suffered recurrences. The Data has been roughly categorized into 27 characteristics that are important for illness staging and therapy. Thevery important attributes selected are FIGO 2009, chemotherapy,radiotherapy, recurrence confirmed by a pathologist, etc. Five different models are trained to predict the progression. Logistic regression is finally predicted with a high level of accuracy of 96.3%. Two hundred and sixty patients with 27 attributes consistof a number of missing values. Research is implemented through the Orange platform. The missing values are balanced through Average and Most frequent data imputation method. Important attributes are selected through the ReliefF technique, and then disease progression is predicted by 5 different algorithms Logistic regression, Support vector machine, Naive Bayes, Stochastic gradient decent and neural network. Finally logistic regression predict the progression of a disease with highest accuracy of 96.3%. © 2023 IEEE.

关键词： Radiotherapy

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The Novel Protocol Uses Low Computational Complexity to Authenticate Edge Devices on Smart Grid Systems 9

The Novel Protocol Uses Low Computational Complexity to Auth...

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9th IEEE India International Conference on Power Electronics, IICPE 2023

作者： Kuppuswamy, Prakash Al Khalidi, Sayed Q.Y. Kumar, Purushottam Meeran, Ahmed Ali Shaik Sonia Varshini, Vijaya Computer Science Engineering SRM University Haryana Delhi-NCR India King Khalid University Department of Information Technology Abha Saudi Arabia Jazan University Department of Information Technology Jazan Saudi Arabia Bharathiyar University Computer Science Deparment Tamil Nadu Coimbatore India

ISBN: (纸本)9798350307252

Grid computing has emerged as a powerful paradigm for harnessing distributed computing resources to solve complex problems. In grid computing, various devices across different administrative domains collaborate to provide computational power and storage capacity. Sensor agent devices have become the backbone of modern technological systems such as Internet of Things (IoT), smart homes, industrial automation, and autonomous vehicles. These devices collect and process real-time data from the surrounding environment, providing crucial insights for decision-making, monitoring, and control purposes. However, the performance of these devices depends on space complexity and time complexity. Achieving optimum performance in sensor agent devices requires a delicate balance between space complexity and time complexity. Reducing one complexity may increase the other, and vice versa. Light weight authentication device-based algorithms play a vital role in verifying the identity of devices and ensuring secure communication within the grid and that needs to satisfy complexity. These protocols verify the identity of devices and users, ensuring that only authorized entities can access and interact with the data. In this article, we will explore a new light weight authentication protocol based on a block cipher i.e ELBC (Extended Linear Block cipher algorithm). The new authentication protocols are easy to implement and deploy in any kind of environment. The algorithm divides the authentication message into blocks and encrypts them using matrix multiplication. By generating an invertible key matrix and applying encryption and decryption equations, the proposed algorithm ensures secure communication. © 2023 IEEE.

关键词： Authentication

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MuLan: Multimodal-LLM Agent for Progressive and Interactive Multi-Object Diffusion

arXiv

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arXiv 2024年

作者： Li, Sen Hsieh, Cho-Jui Wang, Ruochen Cheng, Minhao Zhou, Tianyi Department of Computer Science & Engineering HKUST Hong Kong Department of Computer Science UCLA United States College of Information Science & Technology PSU United States Deparment of Computer Science UMD United States

Existing text-to-image models still struggle to generate images of multiple objects, especially in handling their spatial positions, relative sizes, overlapping, and attribute bindings. To efficiently address these challenges, we develop a training-free Multimodal-LLM agent (MuLan), as a human painter, that can progressively generate multi-object with intricate planning and feedback control. MuLan harnesses a large language model (LLM) to decompose a prompt to a sequence of sub-tasks, each generating only one object by stable diffusion, conditioned on previously generated objects. Unlike existing LLM-grounded methods, MuLan only produces a high-level plan at the beginning while the exact size and location of each object are determined upon each sub-task by an LLM and attention guidance. Moreover, MuLan adopts a vision-language model (VLM) to provide feedback to the image generated in each sub-task and control the diffusion model to re-generate the image if it violates the original prompt. Hence, each model in every step of MuLan only needs to address an easy sub-task it is specialized for. The multi-step process also allows human users to monitor the generation process and make preferred changes at any intermediate step via text prompts, thereby improving the human-AI collaboration experience. We collect 200 prompts containing multi-objects with spatial relationships and attribute bindings from different benchmarks to evaluate MuLan. The results demonstrate the superiority of MuLan in generating multiple objects over baselines and its creativity when collaborating with human users. © 2024, CC BY.

关键词： Diffusion

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