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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kiranagi, Soumya S Patil, Savitha Sharnbasva University Department of Artificial Intelligence and Data Science Kalaburagi India Sharnbasva University Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350372892

there are hundreds of kinds of plants on Earth, and many of them have medicinal or curative properties. Approximately 80% of the global population continues to rely on traditional medicine. In Ayurveda, the use of herbs as medicine dates back to Vedic times. Recognizable identification of the medicinal plants is crucial for the efficacy of ayurvedic, social, and homoeopathic treatments. For the treatment of skin conditions, lung infections, asthma, dyspepsia, and other ailments, many people throughout the globe rely on medicinal plants. One of the most important steps in making an Ayurvedic medicine is finding the right medicinal leaf. Misclassification or incorrect identification of medicinal leaves could affect Ayurveda's efficacy. Form, colour, and texture of leaves are the most basic characteristics that must be present to identify a medicinal plant. While the form and venation pattern of individual leaves might vary greatly, members of the same class of leaves tend to have commonalities. In light of this need, here is a model that makes use of an SVM Machine Learning Algorithm to reliably identify and categories medicinal leaves. © 2024 IEEE.

关键词： Plant diseases

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A Robust data Security Model Development to Maintain the Integrity of Information Using artificial intelligence and Cyber Laws

A Robust Data Security Model Development to Maintain the Int...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Kumar, G. Muthu Hemanand, D. Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

The field of artificial intelligence (AI) has seen significant advancements in recent years. These days, artificial intelligence (AI) tools are being utilized by organizations in both the public and commercial sectors all over the world. Individuals, organizations, and society as a whole will reap broad and significant advantages as a result of the capabilities of artificial intelligence (AI) both today and in the near future. Nevertheless, these very same technical advancements give rise to significant concerns, such as the question of how to ensure that artificial intelligence technology is built and implemented in a manner that is in accordance with the applicable data privacy laws and standards. The fast development of artificial intelligence presents substantial hurdles in terms of protecting customers' privacy and the confidentiality of their data. The purpose of this essay is to suggest an all-encompassing strategy for the development of a framework to solve these concerns. First, an overview of prior research on security and privacy in artificial intelligence is presented, with an emphasis on both the progress that has been made and the limits that still remain. In the same vein, open research topics and gaps that need to be addressed in order to improve existing frameworks are recognized. Regarding the development of the framework, the topic of data protection in artificial intelligence is discussed. This includes elaborating on the significance of protecting the data that is utilized in artificial intelligence models, as well as elaborating on the policies and practices that are in place to ensure the data's safety and the methods that are utilized to maintain the data's integrity. Additionally, the security of artificial intelligence is investigated, which includes an analysis of the vulnerabilities and dangers that are present in artificial intelligence systems, as well as the presentation of instances of potential assaults and malevolent manipulations,

关键词： Risk assessment

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artificial intelligence Based Smart Print on Demand (POD)

Artificial Intelligence Based Smart Print on Demand (POD)

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Garai, Pooja Gholap, Sakshi Gaikwad, Nikunj Chavan, Atharva Sagvekar, Janhavi Mane, Atharva Bhise, Suvarna Vasantdada Patil Pratishthan's College Of Engineering and Visual arts Department Of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798331539948

This project presents the development of a cutting-edge Smart Print on Demand system that leverages advanced technologies such as TensorFlow, HTML, CSS, and JavaScript, integrated with the innovative DALL·E 3 model. The primary objective is to revolutionize the print-on-demand industry by introducing intelligent and dynamic content generation for customized print products. The project architecture incorporates TensorFlow, a powerful machine learning library, to enhance the system's ability to understand and respond to user preferences. The use of HTML and CSS ensures an intuitive and visually appealing user interface, providing a seamless and engaging experience for customers interacting with the print-on-demand platform. JavaScript is employed for dynamic and responsive features, enabling real-time updates and interactive functionalities. © 2024 IEEE.

关键词： Tensors

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Utilizing Attention-based Vision Transformer (AtViT) for Brain Tumour Detection

Utilizing Attention-based Vision Transformer (AtViT) for Bra...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Chaubey, Harshit Kumar Arelli, Siri Agrawal, Karan Gopalaiyengar, Srinivasa K. Iiit Computer Science and Engineering Naya Raipur Raipur India Iiit Data Science and Artificial Intelligence Naya Raipur Raipur India

ISBN: (纸本)9798350373844

This research explores the use of Attention based Vision Transformers (AtViT), a type of neural network, for the detection of brain tumour from MRI scans. Traditional methods of tumour detection rely heavily on the expertise of neuroradiologists and often involve time-consuming processes. Our project aims to automate this process, potentially increasing both the accuracy and speed of diagnosis. We used a dataset of 3,064 MRI images that included three different types of brain tumour: Meningioma, Pituitary tumour, and Glioma. By employing a pretrained Vision Transformer model, which we fine-tuned for our specific task, we achieved a high accuracy of 99.61% with the EfficientFormer model. We compared this to the ResNet-50 model, a traditional architecture, which demonstrated the superior capability of Vision Transformers in handling complex image classification tasks like tumour detection. The findings suggest that Vision Transformers can significantly aid in the quick and accurate diagnosis of brain tumour, offering a promising tool for medical professionals. This study lays the groundwork for further exploration into the integration of advanced neural networks in medical diagnostics. © 2024 IEEE.

关键词： Diagnosis

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Advancements in Breast Cancer Detection: A Holistic Evaluation of Deep Learning Models with Histology and Thermal Imaging datasets 7

Advancements in Breast Cancer Detection: A Holistic Evaluati...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Chandra, Sanskriti Baghel, Pallavi Lakra, Nancy Saxena, Shivam Kumar, Santosh IIIT-Naya Raipur Data Science And Artificial Intelligence Chhattisgarh India IIIT-Naya Raipur Computer Science And Engineering Chhattisgarh India

ISBN: (纸本)9798350359299

This research addresses the pressing issue of breast cancer detection, emphasizing the development and evaluation of deep learning models using two distinct datasets. The first dataset involves histology images, where Convolutional Neural Networks (CNN), ResNet50, and a novel Concatenated Model are employed and rigorously evaluated. The second dataset comprises thermal images, providing an alternative perspective for breast cancer detection. The study reveals promising outcomes, with the Concatenated Model demonstrating the highest overall performance on histology images, achieving an accuracy of 87%, precision of 77%, recall of 77%, and an F1-Score of 77%. For thermal images, CNN achieves an accuracy of 80%, albeit with a lower recall of 51%, while ResNet50 attains the highest recall at 67%. These findings underscore the efficacy of deep learning models for breast cancer detection and emphasize the importance of considering diverse datasets and modalities for a comprehensive evaluation, contributing valuable insights to developing robust breast cancer screening systems to enhance early detection rates and save lives. © 2024 IEEE.

关键词： Infrared imaging

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A Speech Emotion Recognition System for Improved Communication and Enhancing Human-Machine Interaction

A Speech Emotion Recognition System for Improved Communicati...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Deshpande, Manasi Savadekar, Priyanka Marathwada Mitramandal's College of Engineering Savitribai Phule Pune University Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350376470

In today's digital world of remote communication, speech remains one of the most natural ways for human beings to express themselves. However, effective communication involves more than just words;emotions playa crucial role in conveying meaning and intent. Therefore, the detection and analysis of emotions in speech have become essential for understanding and improving communication in various applications. Emotion detection is a complex task due to the subjective nature of emotions. To address this challenge, this paper presents a Speech Emotion Recognition (SER) system, which comprises a set of methodologies designed to process and classify speech signals with the goal of detecting underlying emotions. To build and evaluate the SER system, we utilize the RA VDESS and TESS dataset, which contains recorded speech data with annotated emotions. The first step involves augmenting the audio data to enhance its diversity and robustness. Subsequently, we employ feature extraction methods and pre-processing techniques to derive meaningful acoustic features from the audio recordings. The processed data is then fed into machine learning models to perform emotion classification. These models are trained on the extracted features and learn to recognize patterns associated with different emotions. The system can be employed in call center environments to analyze caller-agent conversations, helping businesses gauge customer satisfaction and improve service quality. Additionally, it can find applications in improving virtual voice-based assistants, enabling them to understand human emotions and respond accordingly. Overall, this research aims to advance emotion detection in speech and contribute to the development of more emotionally intelligent communication technologies that can enhance human-machine interactions andre mote conversations. © 2024 IEEE.

关键词： Customer satisfaction

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Categorisation by Leveraging CNNs and Remote Sensing Satellite Imagery for Crop Analysis in Arid Environments

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Remote Sensing in Earth Systems sciences 2024年第2期7卷 66-79页

作者： Malhan, Anu Chavan, Yogita Rahulsing Swamikan, Brinthakumari Gupta, Megha Varun Bobade, Sunil Department of Artificial Intelligence and Data Science New Horizon Institute of Technology and Management Department of Computer Engineering New Horizon Institute of Technology and Management

The classification of walnuts in the dry areas of Ganquan Township, Awati County, Xinjiang, is investigated in this research using traditional approaches, remote sensing photos taken by satellite, and convolutional neural networks (CNNs). Thorough field sampling and the collecting of Landsat-8 Level-2 data were necessary for correct analysis due to the different environmental features of the research region, which presented major hurdles. Despite some early difficulties, CNN design revisions led to significant gains in classification accuracy. These modifications included AlexNet, VGG, GoogleNet, ResNet, and EfficientNetV2. When compared to more traditional approaches, ResNet and EfficientNetV2 stood up as the most successful models. Nevertheless, challenges including computational complexity and low-resolution imaging highlight the necessity for more research to improve detection efficiency without sacrificing accuracy. The study emphasises the need to keep looking for new ways to analyse and monitor crops, and it suggests that combining remote sensing with advanced machine learning methods could help improve precision agriculture management and increase crop yields in dry areas. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Categorisation of crop Machine learning Precision agriculture Remote sensing Walnut

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Deep learning-based semantic segmentation of human features in bath scrubbing robots

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Biomimetic intelligence & Robotics 2024年第1期4卷 70-79页

作者： Chao Zhuang Tianyi Ma Bokai Xuan Cheng Chang Baichuan An Minghuan Yin Hao Sun School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology Ministry of EducationTianjin 300401China

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing *** study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog *** investigation focuses on semantic segmentation of human features using deep learning ***,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human ***,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human ***,the network is refined by integrating the spatial feature extraction module and the channel attention module based on *** results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over *** conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

关键词： 3D point cloud Human model LiDAR Semantic segmentation

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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A Multimodal Content Moderation System using Adversarial Machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

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3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

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