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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, data Science, Machine learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe late Blight disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and late Blight are dangerous and widespread potato diseases. We would concentrate on late Blight in this effort. A fatal fungus that harms potatoes is called late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： diseases

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Small Intestine Cancer Prediction using deep learning: A Comparative Study of CNNs and RNNs

Small Intestine Cancer Prediction using Deep Learning: A Com...

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distributed Computing and High Performance Computing (dCHPC), International Conference on

作者： l Sherin Beevi P M Joe Prathap Pulimi Hanith Sai Kumar Reddy Gomasani Bhanu Sai Priya Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of Computer Science and Engineering R.M.D. Engineering College INDIA

Small bowel cancer is one of the rare tumors that often stays untreated until it has spread to a more advanced stage. Cancer is the leading cause of death in the globe. Early detection is critical for successful treatment, and machine learning algorithms have showed promise in detecting cancer at its early stages. In this article, we offer a deep learning-based technique to predicting small intestinal cancer that employs Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We used a dataset of clinical characteristics and biomarkers from small intestinal cancer patients to train and evaluate our models. Our findings reveal that the proposed models were highly accurate in predicting small intestinal cancer, with the CNN-based model slightly outperforming the RNN-based model. We also compared our approach to other cutting-edge methodologies, and discovered that the proposed models surpassed them in terms of accuracy. Our study indicates the potential of deep learning-based approaches for predicting small intestine cancer and provides information on the relative efficacy of CNNs and RNNs. Our findings can help design more accurate and efficient cancer prediction models for small intestinal cancer.

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IOT based Machine learning Algorithms for data Science on Quality Control Process

IOT based Machine Learning Algorithms for Data Science on Qu...

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

作者： Kittusamy, lingarasu lakshmi, G. Prasanna Khatkale, Pravin B William, P. Ghige, Onkar Tingare, Bhagyashree Ashok Compunnel Inc Quality Engineering Lead MorganNJ United States Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani University Maharashtra Kopargaon India Sanjivani College of Engineering Deaprtment of Information Technology Kopargaon India Amity University Dubai United Arab Emirates Sanjivani College of Engineering Department of Elecrical Engineering MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350390049

Because of the combination of Internet of Things (IoT) technology and machine learning (Ml) algorithms, quality control methods in the industrial sector are undergoing a revolution. This is because real-time monitoring, predictive maintenance, and anomaly detection are now entirely feasible. Conventional quality control methods, which often rely on random inspections and sampling, are being phased out in favour of these cutting-edge technologies, which provide continuous data collection and analysis. The purpose of this research is to get a better understanding of how it is possible for IoT and Ml to enhance quality control by examining their applications, benefits, and challenges in industrial settings. In this work, we demonstrate how machine learning algorithms that are based on the Internet of Things (IoT) have the potential to significantly improve defect detection rates, reduce downtime, and optimise manufacturing processes by using a multidimensional research approach that combines empirical analysis and case studies. The findings illustrate how these technologies have a considerable and beneficial impact on decision-making in data-driven quality control processes, as well as on the efficiency of operations and the quality of products available. © 2024 IEEE.

关键词： Smart manufacturing

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Parasitized Malarial Infected Cell detection through Variance Scaling Kernel Initializer Conv2d 4

Parasitized Malarial Infected Cell Detection through Varianc...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： devi, M. Shyamala Umanandhini, d. Joshi, Aparna dontamsetti, Gowtham Shreyas, S. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Pimpri Chinchwad College of Engineering Computer Science and Engineering Pune India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350343670

Medical pathologists physically examine Plasmodium falciparum parasites infected blood cells that have been infected through a magnifying glass as part of the traditional diagnostic process, which is time-consuming and liable to mistakes. CNN and advanced technology have recently been used to conduct research on optimizing this procedure to identify orientation and geometrical information from blood sample particle images. With this view, this research recommends Variance Scaling Kernel Initializer Conv2d (VSKIC) Sequential model for predicting the infected malaria cell. The Malaria Cell Images dataset from KAGGlE, which contains 27,558 visuals of plasmodium parasites has been used for execution in the suggested VSKIC model. For the purpose of predicting a malarial cell infection, images are preprocessed and fitted using the VSKIC model. Three convolutional layers merged with max pooling layer, and three dense layers that are weight initialized with variance scaling kernel initializer are used as preprocessing steps on the malaria cell images for the purpose of identifying the presence of affected malarial tissue. The picture files were separated into training and evaluation images, and the training Malaria Cell images were used to evaluate the effectiveness of the VSKIC model along with the standard conV2d paradigms. While comparing the suggested model with the available CNN methods, the execution observations made with Python on a V100 Nvidia GPU workstation using 64 and 250 training intervals demonstrates that the proposed VSKIC model predicts with the higher accuracy of 98.96% and Area under curve of 97.8%. © 2024 IEEE.

关键词： accuracy AUC ConV2d initializer kernel

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design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

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computers, Materials & Continua 2023年第7期76卷 1309-1320页

作者： Puneet Kumar Mishra Amrita Rai Nitin Sharma Kanika Sharma Nitin Mittal Mohd Anul Haq Ilyas Khan ElSayed M.Tag El din Department of Electronics and Communication Engineering G L Bajaj Institute of Technology and ManegmentGretear NoidaIndia Department of Electronics and Communication Engineering Chandigarh UnievrsityMohaliPunjabIndia Department of Electronics and Communication Engineering NITTTRChandigarhIndia University Center of Research&Development Chandigarh UniversityMohali121102India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah University11952Al-MajmaahSaudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2d atomic layers,are the main focus of this research *** impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm *** modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SIlVACO TCAd with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

关键词： Graphene tunnel field effect transistor(TFET) band to band tunnelling subthreshold swing

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Internet of Things Oriented Human Body Enabled Communication Protocol design for Health Monitoring Services

Internet of Things Oriented Human Body Enabled Communication...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Krishnamoorthy, P. Vasanthi, R. Pavithra, K.N. Jagadeesh, K. Vijayaraj, T. Arumugam, deepak Sasi Institute of Technology and Engineering Department of Computer Science and Engineering Andhra Pradesh Tadepalligudem India St. Joseph's Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Siddharth Institute of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Puttur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Simats Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The rapid advancement of Internet of Things (IoT) technology has transformed healthcare by enabling remote patient monitoring, chronic disease management, and preventive care solutions. Human Body Communication (HBC) stands out as a promising integration within this evolution, utilizing the body's electrical conductivity for seamless data transmission between wearable health devices and healthcare systems. This direct connection ensures secure, real-time monitoring without traditional connectivity constraints. Proposing a protocol design framework, this abstract aims to optimize communication between IoT and HBC technologies to enhance health monitoring services and the framework accommodates a variety of sensors crucial for comprehensive health assessment, including temperature, EEG, ECG, heart rate, pressure, blood glucose, oxygen saturation, respiratory rate, and blood pressure sensors. By integrating IoT and HBC, healthcare enters a new era characterized by improved accessibility, efficiency, and personalized care delivery. This convergence fosters proactive healthcare, empowering individuals to manage their well-being while enabling healthcare providers to deliver timely interventions for better patient outcomes. © 2024 IEEE.

关键词： Internet of things

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Optimization of Path Planning for Automated Vehicles Using SlAM Algorithm 1st

Optimization of Path Planning for Automated Vehicles Using S...

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1st International Conference on data engineering and Machine Intelligence, ICdEMI 2023

作者： Prinslin, l. Selvi, R. Revathi, B. Yuvabharathi, E. Sharmila, l. Department of Computer Science and Engineering Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Dhanalakshmi College of Engineering Chennai India Department of Information Technology Jeppiaar Institute of Technology Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

SlAM (simultaneous localization and mapping) is a technique for automated vehicles that enables simultaneous map building and vehicle localization. The vehicle is able to map out uncharted terrain owing to SlAM algorithms. Engineers use the map information to carry out tasks such as path planning and obstacle avoidance. An autonomous machine needs the ability to plan the motion of a high-level command. For example, if a task is given to explore a particular area, the bot has to plan accordingly to complete the required task. To achieve this level of accomplishment, the path works on an algorithm that solves SlAM and path planning problems. The machine is designed in such a way that it will work as a grass cutter will complete the task in the given frame. The grass-cutting machine creates its own path in the frame and detects obstacle coming in the path. In this way, the SlAM robot is utilized in the application of agricultural field. In the current work, we have designed a 3-d model of a SlAM robot and its various components like the lIdAR Camera, Jetson Nano, lithium-Ion Battery, Motor, and Body design machine. This model is capable of performing grass-cutting operations using a cutting blade and collection of waste in a sack attached to the machine. This machine works on automation based on path planning algorithms using ROS, this eliminates human interference. Various kinds of research papers were reviewed to compare different kinds of mechanisms and various components were analyzed for the final model *** check and verify that the author names are correctly recognized and presented in the correct sequence order, which is [given name, middle name/initial, family name] for the author(s). In addition, please verify that the name(s) and respective affiliation(s) shown on the metadata page are valid and make any necessary amendments if ***, have edited the affiliation of the last author. © The Author(s), under exclusive license to Springer Nat

关键词： SlAM robotics

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Advancing Rose disease diagnosis: A deep learning Framework with EfficientNet-B7 8

Advancing Rose Disease Diagnosis: A Deep Learning Framework ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Poornima, S. divya, R. Krishnan, R. Santhana Jegadeesan, S. Yamini, G. Rajkumar, G. Vinoth Sri Ramakrishna College of Arts and Science for Women Department of Computer Science Coimbatore India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Information Technology Madurai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of English Chennai India J.P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798350376425

This study introduces an advanced deep learning framework for diagnosing rose diseases using EfficientNet B7, a high-performance model pre-trained on the ImageNet dataset. The research aims to automate the identification of four common rose diseases: Powdery Mildew, Black Spot, downy Mildew, and Rust by applying transfer learning with EfficientNet B7. This approach is evaluated using two specialized datasets, the Rose disease dataset and the Rose leaves disease detection dataset. The framework's effectiveness is assessed by comparing its performance against other prominent deep learning models, including denseNet, ResNet, and VGGNet. Performance metrics such as Accuracy, Precision, Recall, and F1 Score are used for this comparison. The results demonstrate that EfficientNet B7 outperforms denseNet, ResNet, and VGGNet in terms of accuracy and reliability in disease classification. EfficientNet B7's efficiency and superior handling of complex feature representations contribute to its enhanced performance, making it a robust tool for rose disease diagnosis. This advanced framework offers significant improvements over existing models in terms of diagnostic accuracy and computational efficiency, providing a valuable resource for early detection and management of rose diseases. The results underscore the potential of leveraging state-of-the-art deep learning models to enhance agricultural practices and support more effective plant health management strategies. © 2024 IEEE.

关键词： Contrastive learning

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Multi-Speaker diarization using long-Short Term Memory Network 3

Multi-Speaker Diarization using Long-Short Term Memory Netwo...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Aafaq, Nayyer Qamar, Usama Khan, Sohaib Ali Khan, Zeashan Hameed Department of Avionics Engineering College of Aeronautical Engineering National University of Sciences and Technology NUST Islamabad Pakistan N.E.D UET Department of Computer & Information System Engineering Karachi Pakistan

ISBN: (纸本)9798350322125

The task of multi-speaker diarization involves de-tection of number of speakers and segregate the audio seg-ments corresponding to each speaker. despite the tremendous advancements in deep learning, the problem of multi-speaker diarization is still far from achieving acceptable performance. In this work, we address the problem by first getting the timestamps employing voice activity detection and sliding window techniques. We further extract the Mel-Spectrograms / Mel-frequency Cepstral Coefficients (MFCC). We then train a long Short-Term Memory (lSTM) network to get the audio embed dings named d-vectors. Subsequently, we employ K-Means and Spectral clustering techniques to segment all the speakers in the given audio file. We evaluate the proposed framework on publically available VoxConverse dataset and report results comparing with similar benchmarks in the existing literature. The proposed model performs better / at par with exisiting techniques despite simpler framework. © 2023 IEEE.

关键词： long short-term memory

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An Efficient Approach of Product Recommendation System using NlP Technique

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Materials Today: Proceedings 2023年 80卷 3730-3743页

作者： Kumar Sharma, Akhilesh Bajpai, Bhavna Adhvaryu, Rachit dhruvi Pankajkumar, Suthar Parthkumar Gordhanbhai, Prajapati Kumar, Atul Department Of Information Technology Manipal University Jaipur Rajasthan India Dr C V Raman University Khandwa India Department of Computer Engineering Marwadi University Rajkot India Alpha College Of Engineering & Technology Khatraj Kalol India Sankalchand Patel College Of Engineering Visnagar India Dr. D.Y. Patil B-School Pune Tathawade India

As we are moving toward an age of digital globalization and online shopping, there is an increasing need for an efficient and reliable system that can help the consumers and the visitors to find their suitable products. Currently, various websites display the searched product when a visitor comes to their website. What we need is a system, which can recommend the products which are like the searched products. This will help the consumer to find out another product in case the item is unavailable, or the searched product is not good enough, or when they would like to look through different similar products. A good recommendation system has been found out to be financially beneficial for the companies also. It is found out that consumer is 35% more likely to buy a product if the recommendation is good enough for consumers. This proposed approach to the problem of the product recommendation system is to make use of the Amazon Apparel database, which contains data of 180,000 apparels. We are going to use NlP technologies and CNN to help in predicting similar products. Title of the product is used as a major attribute during NlP analysis and recommendation. CNN used at last to create a feature vector from product images, and use this vector combined with all the other vectors, for prediction. We compare the distance between vectors of all products and recommend the products with least distance. VGG-16 architecture used to extract the features from the images. © 2021

关键词： Recommender systems

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