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Mobile Blaze: Harnessing Mobile Net Architecture for Efficient Forest Fire detection 2

Mobile Blaze: Harnessing Mobile Net Architecture for Efficie...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Barkunan, S.R. Raja, S.R. Vedamoorthy, R. Kannagi, l. Maniraj, P. Venu, Nookala Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Centre for Open and Digital Education Chennai India Sri Sairam Engineering College India Sri Sairam Institute of Technology Department of Computer and Communication Engineering India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur Tamilnadu India Madhya Pradesh Gwalior India

ISBN: (纸本)9798350384277

The Mobile Blaze methodology is an innovative strategy that employs the Mobile Net architecture to improve the effectiveness of systems designed for detecting forest fires. Given the substantial risks forest fires pose to ecosystems and human safety, there is a pressing need for cutting-edge technologies to ensure prompt detection and response. In this project, we adapt the lightweight and efficient Mobile Net architecture, originally designed for mobile devices, to create a streamlined and high-performance model for forest fire detection. The proposed Mobile Blaze system integrates state-of-the-art deep learning techniques with the unique characteristics of Mobile Net, enabling real-time processing of high-resolution imagery from various sources, including satellite and surveillance cameras. Our model is trained on a diverse dataset of forest fire scenarios, encompassing different environmental conditions and fire intensities. The use of transfer learning facilitates the efficient transfer of knowledge from pre-trained models, enhancing the model's generalization capabilities. To evaluate the performance of Mobile Blaze, extensive experiments are conducted on benchmark datasets, demonstrating superior accuracy and speed compared to traditional approaches. The lightweight nature of Mobile Blaze enables deployment on resource-constrained devices, making it suitable for edge computing applications in remote forest areas. Furthermore, the system is designed to provide reliable fire detection with minimal false positives, ensuring prompt and accurate response to potential fire incidents. Mobile Blaze represents a significant advancement in forest fire detection, offering a scalable and efficient solution that can contribute to early intervention and mitigation efforts. The integration of the Mobile Net architecture showcases the potential for adapting existing deep learning models to address specific challenges in environmental monitoring and disaster management. © 2024

关键词： Fire detectors

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A Secure data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography 2

A Secure Data Encryption Mechanism in Cloud Using Elliptic C...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Sudha, I. donald, Cecil Navya, S. Nithya, G. Balamurugan, Manivannan Saravanan, S. Saveetha Institute of Medical and Technical Sciences - Simats Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science Bengaluru India Raghu Engineering College Department of Computer Science Engineering Visakhapatnam India Department of Cse V. S. B College of Engineering Technical Campus Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Tamil Nadu Avadi India

ISBN: (纸本)9798350306415

Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. © 2024 IEEE.

关键词： Cloud computing cryptography data security ECC encryption

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Advanced detection of Cyber-Physical Attacks in Manufacturing Using lSTM-KNN 2

Advanced Detection of Cyber-Physical Attacks in Manufacturin...

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2nd IEEE International Conference on data Science and Network Security, ICdSNS 2024

作者： Penchalaiah, N. Kumar, Arvind Chaithrashree, A. Saivaraju, Aravindh Bhoopathy, V. lamba, Anil Kumar Annamacharya University Department of Ai&ml Boyanapalli Andhra Pradesh Rajampet India Sca Mriirs Haryana Faridabad India Brindavan College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Tamil Nadu Chennai India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India Geeta University Department of Computer Science and Engineering Delhi NCR Panipat India

ISBN: (纸本)9798350373110

Cyber Manufacturing System (CMS) is an assembly system that could be used in the future. There will be an increase in cyberattacks like the Stuxnet strike as the number of connected devices continues to grow. Current cyber security measures are inadequate to protect CMSs from new cyber-physical threats. This new security flaw can be better understood with the help of CMS's taxonomy for cyber-physical hazards, which is part of the agency's mandate. Preprocessing, feature selection, and training the model are the three steps that make up this proposed method. The data collected from the sensors show the greatest distance between two nearby locations after any outliers have been removed by preprocessing. Feature selection makes advantage of dimensional reduction derived from geometric feature distribution. When training the model, an lSTM-KNN was used. The average accuracy of the suggested method is 93.46%, which is higher than that of lSTM and KNN. © 2024 IEEE.

关键词： Cyber attacks

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Exploring the Hidden Potential of Cyber Security Oriented Intelligent data Hiding in Encrypted Images 1

Exploring the Hidden Potential of Cyber Security Oriented In...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Nirmaladevi, K. Nagarajan, Anandh Tamizharasi, A. Ramya, R. Allirani, P. Sreenivasa Reddy, M. Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Andhrapradesh Madanapalle India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Tamil Nadu Chennai India MLR institute of technology Department of EEE Telangana Hyderabad India

ISBN: (纸本)9798350364590

In recent years, there has been a surge of interest in the possibility of reversible data hiding inside encrypted images due to the promising practical uses of this technology, which include content owner identification, privacy protection, and encrypted picture authentication. data hiding refers to the process of concealing data within a cover material. It is possible to conceal data in visual, auditory, textual, and graphical formats. Pictures, particularly digital pictures, can be used to conceal data. This data may be authentication details or author information, or it might be any text that is relevant to the image. The receiving end has to be able to decipher the encrypted data and restore the original picture. Advanced Cipher logic based data concealing (ACldH) is a data concealing approach that meets this condition;it allows for accurate recovery of the host image. The losslessness of this method makes it well-suited for use in the medical field, the military, and cloud storage. After extracting the concealed data from an encrypted image, the suggested technique, known as ACldH, may restore the original image to its original state without introducing any deformation. These days, reversible data hiding in encrypted graphics is all the rage because of its great quality: you can have your original cover back after removing the hidden data. It safeguards the initial data as well. Reversible data Hiding (RdH) techniques that are now available for encrypted photos may be classified into two groups: those that do not preprocess the images and those that does. In this research, we show how to use adaptive thresholding to increase data concealing capacity and how to retrieve the original picture from a tagged image without distortion. © 2024 IEEE.

关键词： ACldH Advanced Cipher logic Cyber Security data Hiding Encrypted Images Intelligent data Hiding RdH Reversible data Hiding

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Innovative Practice on Tuberculosis detection for Pneumonia diagnosis using Machine learning

Innovative Practice on Tuberculosis Detection for Pneumonia ...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Manju, K. dinesh, P.M. Thiyagarajan, M. Alagarsamy, Manjunathan Subramanian, Balambigai lakshmi, G. Prasanna Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India K.Ramakrishnan College of Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy India Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641032 India Sandip University School of Computer Science and Engineering Maharashtra Nashik India

ISBN: (纸本)9798350390049

One of the top 10 major causes of death is the bacterial disease tuberculosis (TB), which is brought on by Mycobacterium tuberculosis. Acute pulmonary TB manifests as acute tuberculosis pneumonia (TP). Acute TP and non-tuberculous community-acquired pneumonia, however can be mistaken for one another, which can cause TP to worsen if treatment is put off. In order to stem the spread of TB, an early and precise diagnosis of acute TP is essential. Additionally, the development of new diagnostic tools (technology and techniques) as well as the flexible implementation of various therapy plans will aid in lowering the prevalence of TP and advancing the objective of putting an end to the TB epidemic. © 2024 IEEE.

关键词： Machine learning

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data-driven drive Safety with Cloud-based Adaptive Cruise Control using K-Nearest Neighbors Algorithm

Data-Driven Drive Safety with Cloud-based Adaptive Cruise Co...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Choudri, Subramani Roy Nagalakshmi, T.J. Krishna, Peddi Sai Balasubadra, K. Mouleeswaran, S.K. Maniraj, S.P. Vardhaman College of Engineering Department of Computer Science and Engineering Telangana Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chenagalpattu Chennai Tamil Nadu Kattankulathur India R.M.D. Engineering College Department of Information Technology Chennai Tamil Nadu Kavaraipettai India Dayananda Sagar University Department of Computer Science and Engineering Innovation Campus Karnataka Bangalore India Srm Institute of Science and Technology Department of Computer Science and Communication Tamil Nadu Chennai India

ISBN: (纸本)9798350394399

An innovative new technique cloud-based adaptive cruise control (ACC) uses data analysis and cloud computing to make driving safer and more efficient. Current ACC systems in the cloud depend on static models or rules, to handle the ever-changing nature of traffic. It provide a data-driven method for ACC in the cloud that makes use of the k-nearest neighbours (KNN) algorithm to adapt the vehicle's speed and distance based on learning from both past and present driving data. For this ACC system in the cloud and develop a protocol for communication between vehicles and the cloud, as well as a distance and speed controller based on KNN. It tests the method against both traditional ACC and human drivers in a simulated environment to view how well it performs. In a number of traffic situations, the method outperforms both the human driver and traditional ACC in terms of safety, comfort, and fuel economy. The system's cloud-based architecture allows it to access and understand vast volumes of real-time data, providing drivers with an exhaustive and current data of the environment ahead. The system simulates real-world studies to test the effectiveness of the proposed method, demonstrating how it may improve road safety and traffic flow. When data-driven decision-making is combined with ACC it demonstrates how cloud-based technology may be used to improve road safety and advance the field of intelligent transportation. © 2024 IEEE.

关键词： Cloud computing

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Medical Image Classification Using Thermal Images and diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Govinda Rajulu, G. Sharmila, l. Venkatesan, d. Srinivas, K. Krishnaiah, Nallam Kumar, K. laxman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin’s Engineering College Hyderabad India St. Martin’s Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Telangana Secunderabad India Deptartment of Computer Science and Design St. Martin’s Engineering College Hyderabad India

ISBN: (纸本)9798331505134

lung and Colon (l&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer;however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of l&C tumors were classified using a hybrid deep learning (dl) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the lC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known dl models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries. © 2024 IEEE.

关键词： lung cancer

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WSPNET:Weakly Supervised learning With Pesudomask For Tea Pest detection 2

WSPNET:Weakly Supervised Learning With Pesudomask For Tea Pe...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Malathi, K. Mohanasundaram, N. Santhosh, R. Manikandan, B. Parthasarathy, V. Saravana Kumar, G. Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Dean-R&D and Industry Relations Coimbatore India Karpagam College of Engineering Department of Electronics and Telecommunications Engineering Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

This article tackles the pressing issue of pest detection in tea farming, focusing on two significant pests: the tea thrips (Scirto thrips dorsalis) and the looper (Hypo Sidra Talca). These pests pose serious risks to the health and yield of tea crops. While tea's economic significance is well recognized, much of the existing research has primarily cantered on larger pests in more controlled environments, leaving a noticeable gap in reliable detection methods for smaller pests that thrive in the dense and diverse settings of tea plantations. To address this imbalance, we propose utilizing advanced deep learning models, particularly the UNet and Enriched UNet architectures, to improve the accuracy of pest detection and minimize false positives. Our approach involves creating a robust segmentation model that integrates attention mechanisms, residual connections, and tailored loss functions designed to tackle class imbalance and enhance boundary detection. By harnessing high-resolution imaging and sophisticated artificial intelligence techniques, this research aims to support sustainable pest management in tea cultivation, ultimately enhancing both crop protection and economic sustainability. © 2024 IEEE.

关键词： Health risks

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed data 2

Integrity Estimation of Twitter Based Event Recognition Usin...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Siva Rama lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

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Adaptive Movie Recommendations: A deep learning Framework with Mood and Sentiment Integration 5

Adaptive Movie Recommendations: A Deep Learning Framework wi...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Karthikeyan, M. Saravana Shankar, S. Vijay Krishnan, R. Santhana Sornavalli, M. Raj, J. Relin Francis Sundaravadivel, P. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331530013

This research study proposes an advanced movie recommendation system that combines deep learning models with mood detection and sentiment analysis for highly personalized suggestions. The system collects data from diverse sources, including user watch history from OTT platforms along with real-time social media sentiment from social media platforms. Additionally, wearable devices track heart rate to infer the user's emotional state during media interaction. The recommendation engine uses a multi-layered approach. Neural Collaborative Filtering (NCF) analyzes watch patterns, while BERT-based embeddings capture key movie metadata such as genres and actors for content-based filtering. The system also employs an lSTM model for mood-based recommendations, integrating heart rate data and social media sentiment to align suggestions with the user's current mood. Key advantages include a hybrid recommendation approach that combines collaborative filtering, content-based suggestions, and real-time mood analysis. The system adapts to both long-term preferences and short-term emotional states, delivering more relevant and personalized results. deployed on scalable cloud infrastructure (AWS EC2), with continuous learning from user feedback, the system ensures dynamic and evolving movie recommendations that enhance user satisfaction. © 2024 IEEE.

关键词： Collaborative filtering

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