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Analysing weather conditions using Firebase

Analysing weather conditions using Firebase

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2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023

作者： Jha, Abhishek Lalan Ashraf, Azhar Verma, Sahil Kaur, Maninder Computer Science & Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Uttaranchal University Dehradun India Department of Computer Science and Applications Guru Gobind Singh College for Women Chandigarh India

ISBN: (纸本)9781839539596

Forecasting Weather to a specific time and location via the use of science and technology for its specific location from the 19th century, millions of humans have started predicting weather forecasting. This technology uses meteorology so that it predicts the weather for the future so that the response of the atmosphere can be recorded for the future purpose. Technology for predicting weather we use barometric pressure, in that which show the current weather condition and cloud cover for Sky condition this prediction model is based on the computer Based model show the records of a wide range of atmospheric will be recorded by having this technology for model prediction is pattern recognition, telecommunication model, performance knowledge, but its skill required the more in this technology for forecasting the chaotic character of the weather which is overpowered by the measurement error by this less accuracy the atmospheric dynamic model field to predicting the model which is already exist for forecasting. As a result, as the time gap between today and the predicted model for the forecast has a time gap so it leads to less accuracy, the range of forecasting prediction for selecting the most likely and accurate outcome in the model contest aid narrows free error in it. The most difficult challenge in the world is to predict the forecasting using the Meteorology virtual application for the most significant difficult Technology. In this paper, we are going to investigate data mining techniques and artificial neural networks, my research paper presents the service-oriented model that the architecture for the weather information system will be linked with the GPS system. Algorithms such as Artificial neural networks and Decision tree algorithms are going to use for prediction model for forecasting by gathering information on meteorological data collection of specific time can use for achieving accuracy therefore, this paper includes of classification of different rules f

关键词： Weather forecasting

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Improved UNET Architecture for Effective COVID-19 Severity Prediction from Pulmonary CT Images 1

Improved UNET Architecture for Effective COVID-19 Severity P...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Ramla, M. Jasmine, R. Anita Ramesh, M. Bai, Femilda Josephin Joseph Shobana SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Chennai India SRM Institute of Science and Technology Department of Computer Science Chennai India Istinye University Department of Computer Engineering Istanbul Turkey

ISBN: (纸本)9798350376517

COVID-19, is one of the deadliest pandemic hit globally crises in the recent years, continues to persist with emerging variants of concern. Due to the virus's high transmissibility, rapid and precise detection of COVID-19 is crucial for timely medical intervention. This research presents an advanced deep transfer learning approach for quantifying the severity of COVID-19 infection from CT lung images. Using computer vision techniques, lung regions were extracted from CT scans through semantic segmentation using SegNet. An enhanced U-Net architecture, integrated with the Atrous Spatial Pyramid Pooling (ASPP) technique, was employed to semantically segment the infected regions of the lungs. Based on the segmented infection areas, the severity of the infection was classified into mild, moderate, and severe. The proposed framework, which incorporates lung extraction followed by Ground-Glass Opacity (GGO) segmentation, achieves the segmentation accuracy of 96% and a prediction accuracy of 98.9%. © 2024 IEEE.

关键词： COVID-19

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A Review of Segmentation and Recognition Techniques for Indian Sign Language using Machine Learning and computer Vision 10

A Review of Segmentation and Recognition Techniques for Indi...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumari, Subhangi Tarlue, Ernest Diallo, Aissatou Chhabra, Megha Mishra, Gouri Shankar Goyal, Mayank Kumar School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

Indian Sign Language (ISL) is a complex and diverse language used by the deaf and hard-of-hearing community in India. ISL recognition and segmentation have been active areas of research in recent years due to the growing demand for assistive technologies for this population. In this review paper, we provide an overview of the various techniques used for ISL recognition and segmentation using machine learning and computer vision. We begin by discussing the challenges involved in ISL recognition and segmentation, such as the variations in sign language gestures, lighting conditions, and background noise. We then review the literature on various techniques used for ISL recognition. We analyze the strengths and limitations of each technique, comparing their accuracy, speed, and robustness against various factors. Finally, we present the recent advancements and future directions for ISL recognition and segmentation © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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Analysis of Skin Cancer Classification using Deep Learning Techniques 1

Analysis of Skin Cancer Classification using Deep Learning T...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Muthuselvi, S. Sumathi, R. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350348637

Skin cancer is defined as abnormal development and proliferation of skin cells. It is in various steps. Malignant skin cancer is classified into different kinds: carcinoma of the basal cell, carcinoma of the squamous, and melanoma or melanocytic carcinoma. Each of these is called by its name because it occurs in specific cells. Cancer tumors can be observed because skin cancer occurs in the upper layer of the skin, the epidermis. Unlike other cancers, these can be detected and treated at an early stage, which is why the death rate from this type of cancer is relatively low. The primary cause of skin cancer is prolonged exposure to ultraviolet rays emitted by radiation. Melanoma along with additional skin cancers are more prevalent in the general population than lung, colon, breast, and prostate tumors. Melanoma is the deadliest form of malignancy of the skin and has a high death rate compared to other skin cancers, although melanoma is less common in the general population. Among the most common skin cancers are non-melanoma skin cancers. Some people have the possibility of developing melanoma later in life because of a large birthmark (large mole) that appears at birth or later due to melanocytes. Therefore, evidence-based automatic identification of cancer of the skin is useful for improving pathologists' precision and competency in their initial phase. In this work, we suggest a model developed by RCNN deep learning for accurately distinguishing between malignant and benign lesions in the skin. First, we implement a filter or kernel programming to remove noise and artifacts;then, we normalize the source pictures and determine characteristics assisting in precise classification. Furthermore, data enrichment builds up the overall count of images, which gets a better classifying rate its precision. The performance of our proposed RCNN model is comparable to other transfer learning approaches like the algorithm AlexNet, ResNet, The VGG model 16, DenseNet, MobileNet,

关键词： Dermatology

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A Survey on Energy efficiency in Cloud Computing Frameworks at Different Platforms 5

A Survey on Energy efficiency in Cloud Computing Frameworks ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Anul Solanki, Kamna Dhankar, Amita Uiet Maharishi Dayanand University Department Of Computer Science And Engineering Rohtak India Maharishi Dayanand University Department Of Computer Science And Applications Rohtak India

ISBN: (纸本)9798350330861

Cloud computing is a paradigm that offers a restricted set of virtualized resources that may be accessed as needed. It is crucial to allocate these resources on target machines in a manner that minimizes both inadequate utilization and excess utilization, as these conditions can result in substantial consumption of energy. The term 'nano data centers' refers to a network of widely dispersed computers that have the capability to both host and distribute material and solicitations in a peer-to-peer manner. The investigation of energy usage in nano servers has not been extensively explored, despite their increasing popularity. Several findings have been derived from our study, which highlight the aspects within the system design that enable nDCs to achieve lower energy consumption compared to their centralized counterparts. This paper presents a comprehensive definition of Cloud Frameworks, encompassing many platforms such as edge computing, green computing, parallel computing, distributed computing, and fog computing. In conclusion, this study highlights a number of obstacles and potential within the realm of cloud computing. It is our intention that this work will attract the interest of the academic community and serve as a catalyst for further investigation in this domain. © 2023 IEEE.

关键词： Fog computing

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Improved Resource Allocation in 6G Networks Through Artificial Intelligence 2

Improved Resource Allocation in 6G Networks Through Artifici...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Gupta, Rolly Kumar Sagar, Pramod Malhotra, Sonal Kulshreshtha, Kushagra Srm Institute of Science and Technology Computer Science & Engineering UP Modinagar India Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Computer Engineering & Applications Up Mathura India

ISBN: (纸本)9798350371055

This paper proposes a unique resource allocation technique for 6G networks the use of synthetic intelligence (AI). To assist the evolution of cellular networks, aid allocation mechanisms must be designed to successfully allocate network assets and assist the large wide variety of to be had offerings. Thus far, this has been implemented with the aid of traditional algorithms which possess an upper restrict on their talents. AI-based useful resource allocation offers superior adaptively and scalability to such algorithms, through gaining knowledge of from historic records and feedback from the person terminals. On this paper, an improved aid allocation algorithm primarily based on the integration of AI technology is proposed, underneath the perspective of optimizing both electricity performance and network overall performance. The proposed set of rules relies on a deep Q-learning framework, taking gain from deep neural networks to dynamically examine the most reliable resource allocation strategy. Simulation consequences display an improvement in network performance and energy performance in comparison to traditional optimization schemes. © 2024 IEEE.

关键词： dynamically mechanisms propose schemes technique

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Energy-efficiency Analysis of Different Scheduling Algorithms for Mobile Crowd Computing 15

Energy-efficiency Analysis of Different Scheduling Algorithm...

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

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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Empowering Dynamic Scheduling in IoT Networks through Collaborative Policy Learning via Federated Reinforcement Techniques 2

Empowering Dynamic Scheduling in IoT Networks through Collab...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Thirunavukkarasu, M. Thaiyalnayaki, S. Jose, Naduvathezhath Nessariose Maheshwari, S. Pavithra, B. Malathi, G. Department of Computer Science & Applications S.A College of Arts & Science India Department of Computer Science Rabiammal Ahamed Maideen College for Women India Denken Solutions India Department of Information Technology Sri Sai Ranganathan Engineering College India Department of Computer science and Engineering Sri Sai Ranganathan Engineering College India Department of Computer Science and Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9798350383867

In the burgeoning landscape of Internet of Things (IoT) networks, efficient management of resources is paramount for ensuring optimal performance and resource utilization. Dynamic scheduling, particularly in the context of cloud-edge-terminal IoT networks, presents a significant challenge due to the diverse and dynamic nature of connected devices and their varying computational requirements. Traditional centralized approaches to scheduling may prove inadequate in such dynamic environments, necessitating the exploration of novel techniques. This project proposes a pioneering approach to address the dynamic scheduling challenges in IoT networks by leveraging collaborative policy learning through federated reinforcement techniques. The proposed framework harnesses the power of federated learning, a decentralized machine learning paradigm, to collectively train policies for dynamic scheduling tasks across distributed edge and terminal devices while preserving data privacy and security. Key components of the proposed framework include a collaborative learning architecture that orchestrates the exchange of policy updates among edge and terminal devices, enabling them to adaptively refine their scheduling policies based on local observations and feedback. Reinforcement learning serves as the underlying mechanism for policy optimization, allowing devices to learn and adapt to evolving network conditions and user demands over time. By decentralizing the learning process and leveraging the collective intelligence of edge and terminal devices, the proposed framework offers several advantages. These include improved scalability, reduced communication overhead, and enhanced resilience to network failures. Furthermore, the federated approach ensures data privacy and regulatory compliance by keeping sensitive information localized to individual devices. To evaluate the effectiveness of the proposed framework, comprehensive simulations and real-world experiments will be conducted u

关键词： Reinforcement learning

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Recent Developments in Botnet Detection Tools and Techniques for IoT Security: A Review 2

Recent Developments in Botnet Detection Tools and Techniques...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Jain, Ruchi Nihalani, Neelu UIT-RGPV Department of Computer Science & Engineering M.P. Bhopal India UIT-RGPV Department of Computer Applications M.P. Bhopal India

ISBN: (纸本)9798350390179

The Internet of Things (IoT) embodies a paradigm shift in technology, connecting a diverse array of devices to enhance operational efficiency and user convenience across industries, educational settings, and urban infrastructures. This paper provides an in-depth examination of IoT's evolution, showcasing its profound impact on transforming traditional practices through interconnected smart systems. Despite its numerous advantages, the widespread adoption of IoT introduces significant challenges in cyber security and privacy. Recent high-profile cyber-attacks have underscored vulnerabilities inherent in IoT networks. This study delves into the growing threat of IoT botnets, sophisticated networks of compromised devices exploited by malicious actors for largescale attacks. It explores cutting-edge detection methodologies, including advanced deep learning models and architectural frameworks such as IoT-BDA, designed to proactively identify and mitigate these emerging threats. By critically analyzing current methodologies and technological innovations, this research aims to bolster awareness and develop robust strategies to safeguard IoT ecosystems. Ultimately, these efforts seek to establish a secure foundation that fosters continuous innovation and sustainable development within the IoT landscape. © 2024 IEEE.

关键词： Bot (Internet)

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AI Enabled Convolutional Neural Networks to Detect Brain Tumors 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Singh, Kamred Udham Kumar, Ankit Kumar, Gaurav Singh, Teekam Kothari, Paras Rao, Ankur School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering and Applications GLA University UP Mathura India Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun India Department of Computer Applications Geetanjali Institute of Technical Studies Rajasthan Udaipur India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

In the early stages of a person’s existence, it is common practice to screen for the presence of brain tumors. Nevertheless, in the current day, it has been improved using various AI computations. Along these same lines, in order to diagnose a patient with a brain tumor, we take into consideration the data of patients, such as MRI scans of the patient’s brain. Our main concern at this point is determining whether or not the tumor is present in the patient’s brain. For a patient to have a healthy lifespan, it is essential for the tumor to be identified at the earliest possible stage. During this phase of the project, we use a calculation called a convolutional neural network to determine the level of severity, which provides accurate results. The division of tumors is an essential and challenging task that falls within the purview of clinical image production. This is due to the fact that human-aided manual characterization might result in erroneous anticipations and discoveries. We proposed the use of a convolutional neural network as a means of differentiating cancers from 2D appealing resonance brain imaging (MRI). The exploratory study was carried out on a dataset that included tumors with a wide range of dimensions, localizations, configurations, and picture forces. Convolutional neural networks, which are built using Keras and Tensorflow, were chosen for usage by our team because, in comparison to regular neural networks, they provide a much-improved presentation. During the course of our investigation, CNN demonstrated an accuracy of 98.73%. Because of its great speculation capacity and quickness, the CNN design that was only recently developed has the potential to be employed as an effective decision-aid device for radiologists working in clinical diagnostics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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