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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Agrawal, Shobit Bhardwaj, Sonika Sharma, Nandini Mishra, Varun Gl Bajaj Group of Institutions Department of Computer Science and Engineering Uttar Pradesh Mathura India Gla University Department of Computer Engineering and Application Uttar Pradesh Mathura India

ISBN: (纸本)9798350305463

Food is essential for humanity and is primarily produced through agriculture. While addressing fundamental human needs, agriculture also serves as a global source of employment. Recent advancements in machine learning (ML) research have not only enhanced agricultural gains but also brought about additional benefits, including resource optimizations. ML, functioning as an advanced technology, provides farmers with detailed advice and insights into crop yield and resources optimization, aiding in the reduction of farming losses. This article comprehensively examines recent ML applications and trends in agriculture, showcasing its potential for more accurate and effective farming, yielding high-quality results with reduced human labor. The findings emphasize the considerable space for innovation in meeting the demands of businesses seeking sustainable and optimized agricultural practices. These technologies, can be crucially helpful in agriculture in automation Furthermore, the paper serves as a potential milestone for future research in various directions of agriculture. © 2024 IEEE.

关键词： Support vector machines

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Multimodal Attention CNN for Human Emotion Recognition 1

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Tiwary, Gyanendra Chauhan, Shivani Goyal, Krishan Kumar Department of Computer Science and Engineering Bhagwant University Ajmer India Department of Computer Application Raja Balwant Singh Management Technical Campus Agra India

ISBN: (数字)9789819706419

ISBN: (纸本)9789819706402

The human face is the mirror of the mind. The face generally tells all that is going on in one’s heart and mind. Just by looking at the faces of our known ones, we may easily guess their mood. But many times, when we meet some unfamiliar person, it’s hard to get his or her mood just by looking at their faces. This is just because the person may have a certain facial structure that makes them by default look angry, happy, or sad. So, we need to spend some time with that person to analyse other parameters before concluding their state of mood. The current work proposed a novel approach that integrates facial images with electroencephalography (EEG) signals for facial expression recognition tasks. When attention-based deep CNN analyses the facial traits of the subject, a parallel Long Short-Term Memory (LSTM) network analyses the EEG signals. A late fusion network combines the features extracted from both networks, and finally, a classification network tells about what is the current mood of the subject. Combining multiple modalities for emotion recognition has shown promising results when compared with other state-of-the-art models. There are multiple real-life applications of emotion recognition models, such as Advertisement Industry, Human–Robot Interaction, Automatic Depression Detection, Mood Audio/Video Players, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Real-Time Face Mask Detection in Video Streams Using Deep Learning Technique 4

Real-Time Face Mask Detection in Video Streams Using Deep Le...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350300888

Face masks are necessary during the worldwide pandemic to prevent the transmission of infectious diseases. This research proposes a deep learning-based system for detecting face masks in live video feeds in real-time. The system's goal is to automatically determine if people on a video broadcast are hiding their faces behind masks. To do this, a deep convolutional neural network architecture is sued and is trained on a huge dataset of annotated photos that include both masked and unmasked faces. The existing systems struggle to handle large-scale deployment and real-time processing of video streams for face mask detection. The network was built to learn features that reliably identify masks or no masks on faces. The transfer learning method is employed for fine-tuning in order to enhance the network's ability to generalize. Further, this study employs a powerful detection pipeline that uses hardware acceleration and parallel processing approaches to deal with the real-time nature of video streams. Since the channel processes video frames in real-time, it can be used in places where fast detection is critical, such as hospitals, airports, and other public buildings. To measure the proposed system's efficacy, large trials are run over various video datasets, each with its unique combination of circumstances and camera angles. The findings show that the proposed Convolutional neural network method is effective, comparing with Dataset-1 and Dataset-2 with real-time processing speed and good accuracy. Solution is accurate 95% to 99% and efficient than the current techniques. © 2023 IEEE.

关键词： COVID-19

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QoS aware task scheduling and congestion avoidance in fog enabled car parking systems

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International Journal of Information Technology (Singapore) 2024年第8期16卷 4787-4795页

作者： Dhananjaya, M.K. Sharma, Kalpana Chaturvedi, Amit Kumar Department of Computer Science Bhagwant University Ajmer India Department of Computer Science and Application Bhagwant University Ajmer India Department of MCA Govt. Engineering College Rajasthan Ajmer India

When multiple cars compete for the same parking space, congestion arises. As it turns out, individuals waste an extensive amount of time looking for parking spaces, which increases fuel consumption and emissions. A few researchers proposed a fog-based smart car parking approach to address the drawbacks of cloud-based approaches. The researchers were unable to reach the global optimum for picking an empty parking space, and they neglected to consider quality of service (QoS) when a fog node can’t process a request. Therefore, to achieve such goals, we proposed QoS aware task scheduling and congestion avoidance in fog enabled car parking systems. This approach breaks down into three layers: the data layer, the fog layer, and the cloud layer. Data from car parking lots are gathered in the data layer using IoT devices. The relevant fog node will receive the gathered data in the fog layer. The task will be scheduled to a different suitable fog node using the multi-objective artificial flora optimization (MoAFO) algorithm. The available free parking spaces in each fog node are also estimated using the adaptive coati optimization algorithm (ACOA). The cloud server layer then stores and manages the data that the fog nodes have collected. The Java platform is used to implement the proposed methodology. The energy, time, searching time, and latency metrics are used to evaluate the effectiveness of the recommended approach. © Bharati Vidyapeeth's Institute of computer applications and Management 2024.

关键词： Adaptive coati optimization algorithm (ACOA) Multi-objective artificial flora optimization (MoAFO) algorithm Quality of service (QoS)

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Approach Towards Web for Exploring the Suitable Job for Individuals 1

Approach Towards Web for Exploring the Suitable Job for Indi...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Goyal, Vishal Tiwari, Akhilesh Jadhav, Sharad K. Yelapure, Shripad Yadav, Ravindra Narasimha Prasad, L.V. Ghaziabad India Krishna Institute of Computer Application and Management Karad India Department of Surgery Karad India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350360707

In light of future work challenges, true human resource management (HRM) must be rebuilt. This involves over time human resource development;it must also contain the concept of sustainability to move from consuming to generating human resources. The labor market is constantly changing, with nontraditional jobs becoming increasingly important, especially in light of the current COVID-19 legislation. A useful teaching strategy in a variety of academic fields, including career development, is experiential learning. Important elements for establishing experiential learning programs at the institutional level are also covered by researchers. Our framework may assist businesses in identifying the type of experiential learning that best fits their objectives and setting for professional training. It can also help ensure that the training is successfully designed and delivered. © 2024 IEEE.

关键词： Resource allocation

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Image Classification in Monkey Species using Image Classifiers of Keras 1

Image Classification in Monkey Species using Image Classifie...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Singh, Neelam Upreti, Manisha Dwivedi, Ayushi Rawat, Vandana Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350352931

A species is biologically basic classification and taxonomic rank of an organism. Among the many common names for the mammals that make up the suborder Simiiformes (sometimes known as the simians), 'monkey' is among the most common. The traditional classification of all members of the group currently known as simians as monkeys. Different classifiers are used to categorize the various kinds of monkeys based on their photos. Assigning a name or class to an image is the work of image classification. In the research paper, different image classifiers of Keras are used for image classification in monkey species. A dataset of 10 distinct monkey species was used to test out many different classifiers. Keras comes preloaded with a variety of Convolutional Neural Networks, including VGG16, VGG19, ResNet50, ResNet-50 V2, ResNeXt101, Inception V3, and Xception. We compared the performance of other classifiers and discovered that ResNeXt-101 achieved the best accuracy (99.98%). © 2024 IEEE.

关键词： Invertebrates

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Breast MRI Registration Using Gorilla Troops Optimization 1st

Breast MRI Registration Using Gorilla Troops Optimization

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1st International Conference on Human-Centric Smart Computing, ICHCSC 2022

作者： Nayak, Somen Mondal, Subhodip Department of Computer Application University of Engineering and Management Rajasthan Jaipur India Department of Computer Science and Engineering University of Engineering and Management Rajasthan Jaipur India

ISBN: (纸本)9789811954023

Metaheuristics assume a significant part in enhancing issues, and most of them are enlivened by the aggregate knowledge of living peculiarities in nature. In excess of 10% of ladies are experiencing bosom disease in their lives in the entire world. Registration of breast magnetic resonance imaging is a method to align pre- and post-contrast images to analysis and classifying of cancer category. In this research, breast magnetic resonance imaging registration using gorilla troops algorithm is proposed. GTO is a metaheuristics-based optimizations algorithms that tested to registering the image of breast magnetic resonance. After that, images are successfully registered using GTO algorithm. The result of GTO-based registration method is compared with registration based on PSO method. The results implicates, in case of registration of breast magnetic resonance images, the GTO-based registration methods beat the PSO-based registration method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Particle swarm optimization (PSO)

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An Enhanced Deep Learning Algorithms for Image Recognition and Plant Leaf Disease Detection 2

An Enhanced Deep Learning Algorithms for Image Recognition a...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Asha, V. New Horizon College of Engineering Department of Master of Computer Application Bengaluru India

ISBN: (纸本)9798350325799

Object recognition in photographs has only been possible with the advent of modern deep learning (DL) techniques. Following its success in other fields, DL techniques are increasingly being used to a broad variety of agricultural operations. Due to its intrinsic complexity, image-based plant disease detection is difficult to solve. Targeted management relies on more than just detection and requires accurate identification of each species. An automated disease detection system aids the farmer by providing a speedy and accurate diagnosis of plant illness. Accelerating agricultural diagnostics requires the development of an automated system for identifying plant leaf diseases. Using machine learning and image processing, the authors of this study outline a strategy for detecting leaf blight. A photo of a leaf may be fed into this framework. In a first processing stage, noise is eliminated from the leaf images. The mean filter may be used to get rid of noise. Histogram equalisation is a method for enhancing image quality. Segmentation is a technique used in photography to break a large image down into more manageable sections. This helps to establish the picture's limits. The image is broken up using the BIRCH (Balance Iterative Reducing and Clustering utilising Hierarchies) technique. For this purpose of feature extraction, fine grained classification model, a convolutional neural network is used. After that, the pictures are categorized using a variety of methods, one of which is RCNN (Region based Convolutional neural network). Using this approach, this study discovered the split ratio, computational time, and as a result RCNN has resulted in a high accuracy of 0.98 when detecting the leaf compared to other algorithms. © 2023 IEEE.

关键词： Iterative methods

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Role of cloud computing in management and education

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Materials Today: Proceedings 2023年 80卷 3726-3729页

作者： Gupta, Awaneesh Mazumdar, Bireshwar Dass Mishra, Manmohan Shinde, Priyanka P. Srivastava, Surabhi Deepak, A. United Institute of Management Prayagraj India Department of Computer Science &Engineering IERT Prayagraj211002 India Department of Computer Application United Institute of Management Prayagraj211010 India Master of Computer Application Department Government College of Engineering Maharashtra Karad415124 India Koneru Lakshmaiah Education Foundation India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

In the current day, information and communication technologies are tightly linked to provide ultimate business solutions. Cloud computing is one of these topics that has recently been hotly contested and carefully explored, but still has a lot of room for further research and growth. Technology-assisted learning is becoming more common, with most educational institutions across the globe using learning management systems, content management systems, virtual networks, and virtual machines to enhance student learning. In this day and age, educational institutions are even using private clouds to improve the student experience. Despite the fact that many educational institutions, colleges, and universities have effectively implemented and incorporated instructional technology, many of them still fall short in a variety of areas. The purpose of this research is to investigate the influence of cloud computing technology on educational institutions and corporate management. © 2021

关键词： Cloud computing

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Classification of Potato Leaf Disease using Deep Learning 3

Classification of Potato Leaf Disease using Deep Learning

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Singh, Neelam Sharma, Deeksha Dev, Saurav Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350354171

Deep learning is one of the subdomains of Artificial Intelligence. With the advantages of automatic learning and feature extraction, in recent years, it has become a hot topic in academic and industrial circles. Meanwhile, this has emerged as one of the hotspots within agricultural plant protection research, including plant disease recognition and pest range assessment. Deep learning in plant disease recognition can avoid the shortcomings of artificial selection of features of the disease spot, making the feature extraction of plant diseases more objective, promoting research efficiency, and increasing technological transformation speed. The goal of this work is to analyze potato leaf diseases using CNN in order to determine the model's optimal performance using a large dataset (10,000 photos) of potato leaves in the analysis. The most effective model will support farmers in making decisions that will keep potato production from declining. This investigation revealed that the model worked better when forty epochs were applied to the dataset of 10,000 photos. Additionally, three different epochs were applied and achieved 100% accuracy for 30 and 50 as achieved 99.97% and 99.98% respectively. By offering recommendations for how to categorise Potato Leaf Disease with the best result, the study made a substantial contribution to the agricultural industry and farmers. While working with big volumes of data, researchers should be concerned about the system because running code takes longer time. To focus this, a dataset including 2152 photos was gathered from Kaggle, with an additional 7848 images sourced from various platforms such as Google and raw data from Potato Field. To determine which epochs performed the best, a range of epochs were applied and results shows that 40 epochs led to 100% accuracy. © 2024 IEEE.

关键词： artificial intelligence deep learning potato disease

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