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International Conference on Computing and Networking technology (ICCNT)

作者： Monica R Mundada S Seema Sowmya B J Sonia Fathima Student Department of Masters of Compiuter Application Ramaiah Institute of Technology Banglore India Department of Computer Science and Engineering Ramaiah Institute of Technology Banglore India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Banglore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Since human skin illnesses are among the most common dermatological conditions worldwide, visual observations are usually the first step in the diagnosis process. Accurate classification of skin biopsies and scrapings requires dermoscopic analysis and clinical screening due to medical criteria. It gets more difficult to classify skin diseases using medical photographs because of the complex forms, wide range of colors, and data security issues. In this study, we offer a dependable approach for the classification of 20 distinct types of skin illnesses, based on an improved Convolutional Neural Network (CNN) model. Our enhanced CNN design exceeds the standard CNN, which only achieves an accuracy of 31.38%, with an astounding accuracy of 98.21%. The accuracy of two benchmark algorithms, AlexNet and VGG19, is also not particularly high (32.14% and 52.30%, respectively). In contrast to traditional CNN models the enhanced CNN performs better, demonstrating its efficacy and dependability in accurately identifying a wide range of skin disorders. including well-known architectures like AlexNet and VGG19. Positive implications of this discovery include the development of more precise diagnostic tools for dermatology.

关键词： Training Visualization Accuracy Computer architecture Skin Classification algorithms Convolutional neural networks Medical diagnostic imaging Standards Diseases

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A Novel Generative AI-Powered Approach for Sentiment Analysis: Enhancing Deep learning Models with Contextual Understanding

A Novel Generative AI-Powered Approach for Sentiment Analysi...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Mahendra Shridhar Naik Chaitra S. N K. S. Shashikala Department of Electronics & Communication New Horizon College of Engineering Bengaluru India Department of Electronics & Communication GM Institute of Technology Davangere India Department of Artificial Intelligence & Machine Learning New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Sentiment analysis, a pivotal aspect of natural language processing (NLP), has seen notable advancements with the advent of deep learning models. Despite these advancements, challenges persist in accurately capturing nuanced emotions and understanding context-dependent sentiments. This paper introduces an innovative method that integrates generative adversarial networks (GANs) into deep learning architectures to enhance sentiment analysis. By leveraging GAN’s ability to generate contextually relevant text, the proposed approach enriches the training data, thereby improving the model's ability to discern subtle sentiment variations. Our extensive experiments on benchmark datasets reveal that our method surpasses traditional deep learning models, achieving a 5% improvement in accuracy, a 7% higher F1-score, and demonstrating superior robustness to sarcasm and irony.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Training data Benchmark testing Generative adversarial networks Robustness Data models Context modeling

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Optimal Band Selection and Scale based Feature Selection for Hyper Spectral Image Classification using Hybrid Neural Network 3

Optimal Band Selection and Scale based Feature Selection for...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Jeenath Shafana, N. Jayan, K.T. Divagar Iyyappan, R. Srm Institute of Science and Technology Ramapuram Campus Dept of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Ramapuram Campus Dept of Comp. Sci. and Eng. with Specialization in Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

The use of hyper spectral remote sensing to categorize images has been a popular study topic. Non-linear features and high dimensionality are common in Hyper Spectral Image classifications. Band selection can be used to cut computation costs and speed up knowledge discovery. In hyperspectral photographs, mixed pixels often include some ambiguity. This paper suggests a new band selection procedure method to address these issues. Band selection will be a useful tool for lowering the size of hyperspectral data while also assisting us in overcoming dimensionality issues. To automate hyperspectral picture analysis, this article uses a hybrid model. For ground/landcover classification using hyperspectral pictures, this suggested approach employs strategic and competitive theory models. In a classifier-ensemble system, there are also game theory application models for hyperspectral band grouping and pixel classification. © 2022 IEEE.

关键词： Image classification

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Securing Digital Governance: A Deep learning and Blockchain Framework for Malware Detection in IoT Networks

Securing Digital Governance: A Deep Learning and Blockchain ...

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Integrated intelligence and Communication Systems (ICIICS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula Shwetha K S Department of Information Technology University of the Cumberlands Williamsburg KY USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models.

关键词： Deep learning Data privacy Power demand Smart cities Malware Blockchains Classification algorithms Internet of Things Security Interoperability

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Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors

arXiv

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arXiv 2022年

作者： Hasecke, Frederik Colling, Pascal Kummert, Anton Faculty of Electrical Engineering University of Wuppertal Germany Department of Artificial Intelligence and Machine Learning Aptiv Wuppertal Germany

Segmentation of lidar data is a task that provides rich, point-wise information about the environment of robots or autonomous vehicles. Currently best performing neural networks for lidar segmentation are fine-tuned to specific datasets. Switching the lidar sensor without retraining on a big set of annotated data from the new sensor creates a domain shift, which causes the network performance to drop drastically. In this work we propose a new method for lidar domain adaption, in which we use annotated panoptic lidar datasets and recreate the recorded scenes in the structure of a different lidar sensor. We narrow the domain gap to the target data by recreating panoptic data from one domain in another and mixing the generated data with parts of (pseudo) labeled target domain data. Our method improves the nuScenes (Caesar et al., 2020) to SemanticKITTI (Behley et al., 2019) unsupervised domain adaptation performance by 15.2 mean Intersection over Union points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate a similar improvement for the SemanticKITTI to nuScenes domain adaptation by 21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation. Furthermore we successfully apply our proposed method to two entirely unlabeled datasets of two state of the art lidar sensors Velodyne Alpha Prime and InnovizTwo, and train well performing semantic segmentation networks for both. © 2022, CC BY-NC-ND.

关键词： Optical radar

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An Investigation on CNN-based Lung Cancer Prediction Method

An Investigation on CNN-based Lung Cancer Prediction Method

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International Conference on Computer Communication and Informatics (ICCCI)

作者： R. P. Ram Kumar Sanjeeva Polepaka Dukkipati Likithasree Sabbineni Keerthika Department of Artificial Intelligence & Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India Department of AI & MLE Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India

Diagnosis of any disease is done only after detection of the type and intensity of the disease. Chronic pulmonary disease such as Chronic Obstructive Pulmonary Disease (COPD), lung cancer, and cystic fibrosis needs to be detected in the initial stages to get proper treatment. However, early diagnosis of cancer in the case of lungs is not very easy due to the presence of no recognizable symptoms until much more adverse situations. The proposed work aims at detecting the lung cancer using the Convolutional Neural Network (CNN) regarding the dataset compiled from Kaggle Domain. Various parameters are used to determine the performance of the proposed method, namely, precision, recall and F1-score. Experimental results concluded that, the proposed CNN-based lung cancer detection approach resulted in significantly improved performance when compared to few existing methods. The proposed method has 94% of average precision, 93.6% of recall and 94.6% of F1-score, respectively.

关键词： Training Computational modeling Data preprocessing Lung cancer Lung Predictive models Planning

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Role of RPA & AI in Optimizing Network Field Services 7

Role of RPA & AI in Optimizing Network Field Services

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7th IEEE International Conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Yendluri, Dileep Kumar Ponnala, Jainath Tatikonda, Reshmi Kempanna, M. Thatikonda, Ramya Bhuvanesh, A. University of the Cumberlands Computer Science United States Jawaharlal Technological University Ece Hyderbad India University of the Central Missouri Computer Information Systems Warrensburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India University of the Cumberlands Information Technology Williamsburg United States Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350343144

The paper investigates incorporating and implementing RPA and AI technologies within NFS to improve efficiency and boost service quality. Robotic Process Automation enables the streamlining of repetitive processes. It simplifies work processes and releases personnel for more critical projects. On the contrary, AI strengthens NFS providers via data-informed decision-making. Additionally, it facilitates anticipatory maintenance and preemptive network administration. The document explores particular RPA and cognitive computing instances, including AI-powered network issue resolution and AI-driven preventive maintenance for the network hardware. Furthermore, it emphasizes the advantages and constraints of adopting robotic process automation and artificial intelligence within the NFS framework. It considers aspects including data handling, growth potential, and moral implications. Prospects for the future related to RPA and AI within the NFS domain are also analyzed. It foresees self-governing network management, sophisticated predictive analytics, and merging with IoT and edge computing technologies. The article highlights the revolutionary capability of robotic process automation and artificial intelligence in transforming the field service sector, aiming for increased efficiency, reliability, and a customer-centric approach. It emphasizes embracing these technologies to keep up in the dynamic business sector. © 2023 IEEE.

关键词： Decision making

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The Role of AI and machine learning in Cancer Diagnosis and Treatment

The Role of AI and Machine Learning in Cancer Diagnosis and ...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Tejas Gawande Prateek Verma Department of Computer Science and Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artifical Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget

关键词： Technological innovation Accuracy Federated learning Magnetic resonance imaging Oncology Data models Medical diagnosis artificial intelligence Medical diagnostic imaging Cancer

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Determining the Optimal Parameter for Wheat and Paddy Crops using Smart Farming: An Investigation

Determining the Optimal Parameter for Wheat and Paddy Crops ...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： R. P. Ram Kumar Sanjeeva Polepaka Syed Khasim Numaan Manasa Kumari Srigana Pulikantham Mohith Peddineni Department of Artificial Intelligence & Machine Learning Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India Department of AI & ML Engineering Gokaraju Rangaraju Institute of Engineering and Technology (GRIET) Hyderabad India

Automation is a way to communicate with computers and mobile phones to monitor simple aspects of life. One such thing is the Smart Cultivation. One of the most important factors is it’s very cheap, can be installed anywhere and ensures safety. India contributes a significant share of Agriculture with its allied sectors and is the largest source of livelihood in India. Seventy percent of its rural households primarily rely upon agriculture for their livelihood, with Eighty-Two percent of farmers being small and marginal. When people try to plant and set up their own farm, they will be very cautious at maintaining them in their early stages. But later on, they will neglect and crops will get destroyed. The main objective is to identify the soil features, then predict the crop using KNN algorithm and then maintain the garden automatically using precision agriculture. It plays a very important role and serves as a good companion for the crops. The Internet of Things (IoT) allows us to remotely control and sense things. It consists of a microcontroller through which other sensors are monitored. The Smart Garden consists of ESP 32 and makes use of GISMO-VI Board. All the other sensors like temperature, soil moisture is connected. Firebase is the database on the Internet to which values from the sensors are updated every second. A mobile application is built through Kodular, adopted to develop the proposed method. Through Firebase, the collected data is stored and retrieved for subsequent processing. The data collected through Firebase can be used to monitor the parameters from anywhere. For future enhancements, Soil NPK sensors can be installed to supply the required fertilizers to the garden precisely. Also plant health identification can be done using web- scraping and image augmentation technique is used to increase the amount of data. Finally, a two-class classification (healthy / unhealthy) problem is solved.

关键词： Temperature sensors Temperature measurement Computers Smart agriculture Soil moisture Crops Safety

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Semantic Segmentation of Areal Images using Pixel Wise Segmentation

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Procedia Computer Science 2025年 259卷 463-472页

作者： Swathi Gowroju V. Swathi Narsimhulu K. Shilpa Choudhary Associate Professor Department of Artificial Intelligence & Machine Learning Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Sreyas Institute of Engineering and Technology Hyderabad Telangana Assistant Professor Department of Computer Science Matrusri Engineering College Hyderabad Telangana Associate Professor Department of Computer Science NGIT Hyderabad Telangana

Particularly in regions hit by flooding, precise semantic segmentation of aerial photographs is essential for post-disaster evaluation and urban planning. The high-resolution aerial photography collection FloodNet, created for pixel-by-pixel segmentation of flooded and non-flooded landscapes, is presented in this work. Aerial photos taken from various geographic regions make up the collection, and segmentation masks are used to detect flooded roads, houses, cars, plants, and other vital infrastructure. In order to conduct semantic segmentation on aerial photos and capture the fine-grained information necessary for flood damage assessments, the suggested model makes use of a U-Net architecture. Morphological post-processing is also used to improve object delineation and smooth segmentation borders. The model showed consistent performance with little overfitting, achieving training accuracy of 84% and validation accuracy of 81% over 200 epochs. Effective generalization to unknown data was demonstrated by the validation loss stabilizing at 0.55 and the training loss declining from 1.1 to 0.45. The findings show that the suggested method can reliably identify and categorize areas damaged by flooding from aerial photos, enabling automated flood assessment and speeding up disaster relief activities. There is a lot of promise for integrating this model and dataset combination into real-time catastrophe monitoring, urban planning, and geospatial analytic pipelines.

关键词： Satellite images areal images semantic segmentation UNet using morphology Pixel wise segmentation

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