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Identification and Detection of Leaf Miner, Pests Infestation in Cucurbitaceae Family in Real Time Infield Scenarios using YOLOv5s Object Detection Model 18

Identification and Detection of Leaf Miner, Pests Infestatio...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Keerthi Prasad, M.A. Shobha Rani, N. Sangamesha, M.A. Vinay, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India The National Institute of Engineering Department of Chemistry Mysuru India The National Institute of Engineering Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

This study contributes to technological developments in precision agriculture by developing a YOLOv5sbased model with a high F1 score and mAP, highlighting its potential for real-world use in wax gourd plants. In precision agriculture, early disease identification is crucial for effective harvesting. The present research focuses on the creation and evaluation of a YOLOv5s-based model for detecting pests and leaf miner infections in wax gourd plants in real time infield conditions at various stages of plant growth. Using a dataset of over 2000 samples collected in a variety of in-field settings, the model was trained for 200 epochs with a low learning rate (0.00001) and a batch size of 32. The achieved F1 score of 0.96 demonstrates the model's remarkable combination of precision and recall, ensuring precise disease detection. Furthermore, a mean Average Precision (mAP) greater than 0.45 demonstrates the model's proficiency in precise leaf localization and overall detection ability. The model's ability to detect numerous occurrences of disease in different leaves simultaneously is a crucial strength, reflecting the complexities common in real-world agricultural scenarios. The dataset's quantity and diversity help the model be robust and generalizable across diverse infection types and growth stages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： crop health monitoring Data-driven agriculture object detection precision agriculture Smart agriculture sustainable farming

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Hybrid Multiple Cryptography for Data Encryption 8

Hybrid Multiple Cryptography for Data Encryption

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Anuradha, M. Loganathan, Sathyapriya Suseela, G. Selvan, Mercy Paul Nalini, M. Chitra Devi, D. S.A.Engineering College Dept of Cse India Faculty of Engineering and Technology Srm Institute of Science and Technology Computing Technologies India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept of Networking and Communications India Sathyabama Institute of Science & Technology Cse Department India

ISBN: (纸本)9798350396638

Since the information technology revolution, cryptography has been essential for protecting sensitive information and communication. A plethora of algorithms have been developed to safeguard data and protect data integrity, with the goal of improving data security and protection. Modern cryptography is based on a variety of encryption techniques, namely Hybrid cryptography and multiple cryptography. Hybrid cryptography combines asymmetric key and symmetric key encryption to combine the strengths of both. Multiple cryptography is the process of encrypting a previously encrypted message one or more times, each time using the same or a different symmetric or asymmetric algorithm. The study proposes an approach that seeks to incorporate both hybrid and multiple cryptography as a way to combine all of their benefits while also minimizing the drawbacks that each technique has on its own to provide an additional layer of security. Although these techniques have all contributed to data protection, not all of them have been successful in providing a high level of security as they have shown a propensity for being targeted and exploited over time. This research project implements a cryptosystem based on Hybrid Multiple Cryptography (HMC) which is a novel scheme for data encryption that offers a greater level of security for sending data in an Android messaging application, in order to showcase the system's security capabilities in real time. © 2023 IEEE.

关键词： Cryptography

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Unveiling Security Vulnerabilities in NFTs: A Comprehensive Risk Assessment 4

Unveiling Security Vulnerabilities in NFTs: A Comprehensive ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Verma, Ravi Chandrawanshi, Kamlesh Soni, Gaurav Jain, Garima Nigam, Shekhar Jain, Neelesh Vit Bhopal University School of Computing Science and Engineering Bhopal India Samrat Ashok Technological Institute Department of Computer Science Vidisha India Sam Global University School of Computer Science and Engineering Bhopal India Prestiage Institute of Science and Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331518981

The biggest invention of Non-Fungible Tokens System (NFTs), that defines its role digitally using the self-generated concept of proof of ownership, which comes as a result of continues progress and advancement in Blockchain Technology This study aims to provide a comprehensive understanding of NFTs, their foundational technology, and the platforms enabling their trade. It highlights the key challenges of implementing NFT technology, focusing on issues related to ownership, governance, security, privacy, and environmental concerns. The research explores the inherent risks in the evolving NFT ecosystem, offering insights into the nature and scale of these threats. Additionally, it proposes a set of actionable solutions for participants and stakeholders to mitigate these risks. By categorizing core hazards within a simplified taxonomy, this paper identifies key vulnerabilities and outlines strategies for addressing them to enhance the safety and sustainability of the NFT space. © 2024 IEEE.

关键词： Differential privacy

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VQGAN Text-to-Image Generation Enhancement using CLIP

VQGAN Text-to-Image Generation Enhancement using CLIP

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Prathaban, Banu Priya Subash, R. Ashwini, A. Shahila, D. Ferlin Deva Prasanna, R. Srm Institute Of Science And Technology Networking And Communications School Of Computing Kattankulathur India Srm Institute Of Science And Technology Computing Technologies School Of Computing SRM Nagar Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute Of Science And Technology Department Of Ece Chennai India Srm Institute Of Science And Technology Department Of Electronics And Communication Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798350352689

Text-to-image synthesis is a computer vision task that involves understanding and converting textual descriptions into corresponding and relevant images. Recently, Generative Adversarial Networks (GANs) and Contrastive Language-Image Pre-training (CLIP) have been employed to accomplish this objective. VQGAN comprises of a generator and a discriminator network that work together in a competitive manner to create realistic images based on input descriptions. In contrast, CLIP is a transformer-based language model that has been pre-trained using a diverse set of text and image data. Integrating the capabilities of both VQGAN and CLIP can lead to notable enhancements in text-to-image generation compared to using either technique individually. This abstract offers a concise overview of the latest progress in this area and emphasizes the potential advantages of leveraging a combination of GANs and CLIP for text-to-image synthesis. © 2024 IEEE.

关键词： Generative adversarial networks

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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Multiple moving object classification and tracking using DenCNN classifier

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期46卷 11311-11329页

作者： Premanand, V. Arulalan, V. Kumar, Dhananjay School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur India Department of Information Technology Anna University Madras Institute of Technology Campus Tamil Nadu Chennai India

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy. © 2024 - IOS Press. All rights reserved.

关键词： Convolution

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Optimized Deep Learning Framework for Personalized Nutritional Recommendations Across the Menstrual Cycle 5

Optimized Deep Learning Framework for Personalized Nutrition...

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

作者： Logapriya, E. Surendran, R. Poornima, D. Kartheesan, L. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

This study presents a deep learning architecture designed to monitor a woman's menstrual cycle and provide personalized dietary suggestions to improve health. The system employs complex models, including CNNs, RNNs, Transformers, T-GNNs, VAEs, and GANs, to predict the intensity of symptoms, determine the phases of the menstrual cycle, and offer personalized nutritional advice. By analysing data from menstrual diaries, food consumption, and symptom tracking, the method establishes a strong association between hormonal fluctuations, symptoms, and nutritional needs. Model accuracy is enhanced through data preprocessing techniques, including imputation, normalization, and augmentation, which provide high-quality training inputs. Research shows that Transformer models outperform other approaches in phase recognition (96% accuracy) and phase-aware nutritional recommendation accuracy. This research highlights how deep learning can transform women's healthcare by providing personalized nutritional recommendations based on data that align with the phases of a woman's menstrual cycle, ultimately contributing to improved health for women. © 2024 IEEE.

关键词： Generative adversarial networks

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Predicting Epileptic Seizures with Precision A Comprehensive Study of ML Algorithms 2

Predicting Epileptic Seizures with Precision A Comprehensive...

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

作者： Ranjani, M. Ramya, G. Divya, G. Deepa, N. Lalitha, B. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Chennai India Kpr Institute of Engineering and Technology Department of Eee Coimbatore India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384277

Epilepsy, a complex neurological disorder, poses a significant challenge in the realm of healthcare. Encephalography (EEG) stands as a widely adopted clinical tool for recording the intricate electrical activity within the brain. This research delves into the application of machine learning techniques for the prediction of epileptic seizures using EEG signals obtained from the UCI Machine Learning Repository. Our study presents a comprehensive evaluation of various ML algorithms, including the SVM Classifier, RF Classifier, Gaussian Naive Bayes, K-Nearest Neighbors, and others. Significantly, the Support Vector Classifier and Random Forest Classifier achieved remarkable accuracies of 0.98, suggesting their potential for precise prediction. Meanwhile, Gaussian Naive Bayes and K-Nearest Neighbors demonstrated robust predictive capabilities, with accuracies of 0.96 and 0.93, respectively. This research contributes substantially to the field of epilepsy prediction, shedding light on the effectiveness of diverse machine-learning models for early seizure detection. These findings hold promise for enhancing patient care and improving their quality of life, marking the beginning of an exciting journey towards refined epilepsy management © 2024 IEEE.

关键词： Adversarial machine learning

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A comprehensive Study on Security Threats in Autonomous Vehicles: Safeguarding the Future 1

A comprehensive Study on Security Threats in Autonomous Vehi...

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1st International Conference on Cognitive, Green and Ubiquitous computing, IC-CGU 2024

作者： Poddar, Srisha Deepak Murali, M. Raj Asekaran, P. Prabakaran, N. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Sona College of Technology Department of Information Technology Salem India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chengalpattu603203 India

ISBN: (纸本)9798350361780

Autonomous vehicles, commonly referred to as self- driving vehicles, have the capability to operate and perform necessary functions without the need for human intervention. This is made possible with advanced technologies such as cameras, ultrasonic and infrared sensors, INS, GPS, DSRC, RADAR, LiDAR, and onboard computers, which work together to map the vehicle's position and surroundings. However, because these technologies are easily susceptible to manipulation, autonomous vehicles are at risk of cyber-attacks if hackers succeed to find vulnerabilities in the vehicle's system or in the electronic system of the company producing the vehicle. This study aims to investigate how machine learning and deep learning methods can be utilized to improve the defence against cyber threats aimed at autonomous vehicles by analysing several computational algorithms. © 2024 IEEE.

关键词： Autonomous vehicles

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Numerical Simulation Study of Oil-Water Emulsion Separation in an Ultrasonic Field:Effect of Coupling between Acoustic and Flow Field Parameters

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China Petroleum Processing & Petrochemical Technology 2024年第1期26卷 116-125页

作者： Wang Heping Lin Yinchao Li Yanggui Zhang Xiaohang Wu Yi Key Laboratory of Engineering Mathematics and Advanced Computing of Nanchang Institute of Technology School of SciencesNanchang Institute of TechnologyNanchang 330099People’s Republic of China Dongguan Key Laboratory of Kunpeng Computing School of Computer ScienceGuangdong University of Science&TechnologyDongguan 523083China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016China

In this study,the separation and coalescence of oil-in-water emulsions are explored in an ultrasonic field using the lattice Boltzmann *** simulating the propagation of ultrasonic waves,this study focuses on examining the effects of acoustic wave frequency,the ratio of oil to water components,and the aspect ratio of the boundary on the emulsification and separation processes of oil-water *** following conclusions are drawn.①Frequency affects the speed of oil droplet separation,leading to an increase in droplet size over *** droplets are found near the source,while smaller droplets are distributed throughout the wave web.②As the boundary aspect ratio increases,the emulsification efficiency of the droplets weakens,and the system takes longer to stabilize.③Emulsions with a higher component of oil can better resist acoustic waves.④At the same acoustic frequency,longer wavelength ultrasonic fields promote the formation of uniformly distributed,smaller oil droplets,which is beneficial to the storage of *** numerical simulation results offer insights for optimizing conditions for oil-in-water separation and serve as a numerical reference for the study of oil-in-water emulsion separation in ultrasonic environments.

关键词： lattice Boltzmann method phase separation ultrasound field

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