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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Vooradi Sandya Veeresh Baligeri Bechoo Lal Vishwanath Petli Pradeep Kumar S Department of CSE CMR Technical Campus Hyderabad India Department of Computer Science & Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Computer Science & Engineering K L University Vadeeshwaram Vijayawada India Department of Electronics and Communication Engineering H. K. E. Society's S. L. N. College of Engineering Raichur India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Classification, preprocessing, feature extraction, and segmentation are all parts of the planned study that will be utilized to categories and detect brain tumor pictures. Magnetic resonance imaging (MRI) gives direct information about anatomical structures as well as possibly abnormal tissues where patients are being watched by physicians, making brain tumor identification not as much simpler for clinical diagnosis. This suggested system utilizes a machine learning strategy to identify, and categories brain tumors known as gliomas. Kirsch's edge detected pixels are used to identify the edges of the boundaries. Using this improved brain scan, the ridge let transform is used to extract the ridge let multi-resolution coefficients. As an added step, the ridge let converted coefficients are used to create features, which are then improved with the help of the CANFES classifier. Evaluation factors like as sensitivity, specificity, and accuracy are applied to the results in the context of tumor detection. Both the old approach and the suggested methodology are implemented in simulation using a programming environment like MATLAB, and the results of these simulations are compared to demonstrate the efficacy of the proposed algorithm. The suggested tumor detection approaches employing Co-Active Adaptive Neuro-Fuzzy Expert System Classifier have an accuracy of 98.73%, which offers iv accurate detection of the tumor, and so should be regarded as superior to the current traditional procedures.

关键词： Training Adaptation models Three-dimensional displays Adaptive systems Image edge detection Magnetic resonance imaging Transforms

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V-LTCS: Backbone exploration for Multimodal Misogynous Meme detection

Natural Language Processing Journal

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Natural Language Processing Journal 2024年 9卷

作者： Sneha Chinivar Roopa M.S. Arunalatha J.S. Venugopal K.R. Department of Computer Science and Engineering Bangalore University Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering University Visvesvaraya College of Engineering (UVCE) Bengaluru India Bangalore University Bengaluru India

Memes have become a fundamental part of online communication and humour, reflecting and shaping the culture of today’s digital age. The amplified Meme culture is inadvertently endorsing and propagating casual Misogyny. This study proposes V-LTCS (Vision- Language Transformer Combination Search), a framework that encompasses all possible combinations of the most fitting Text ( i.e. BERT, ALBERT, and XLM-R) and Vision ( i.e. Swin, ConvNeXt, and ViT) Transformer Models to determine the backbone architecture for identifying Memes that contains misogynistic contents. All feasible Vision-Language Transformer Model combinations obtained from the recognized optimal Text and Vision Transformer Models are evaluated on two (smaller and larger) datasets using varied standard metrics ( viz. Accuracy, Precision, Recall, and F1-Score). The BERT-ViT combinational Transformer Model demonstrated its efficiency on both datasets, validating its ability to serve as a backbone architecture for all subsequent efforts to recognize Multimodal Misogynous Memes.

关键词： Misogynous Memes Neural network classifier Vision-language transformer model

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Symptom Based Health Prediction using Data Mining 4

Symptom Based Health Prediction using Data Mining

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4th International Conference on Communication and Electronics Systems, ICCES 2019

作者： Vijava Shetty, S. Karthik, G.A. Ashwin, M. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781728112619

The general day to day health of a person is vital for the efficient functioning of the human body. Taking certain prominent symptoms and their diseases to build a Machine learning model to predict common diseases based on real symptoms is the objective of this research. With the dataset of the most commonly exhibited diseases, we built a relation to predicting the possible disease based on the input of symptoms. The proposed model utilizes the capability of different Machine learning algorithms combined with text processing to achieve accurate prediction. Text processing has been implemented using Tokenization and, is combined with various algorithms to test the similarities and the outputs. In health industry, it provides several benefits such as pre-emptive detection of diseases, faster diagnosis, medical history for review of patients etc. © 2019 IEEE.

关键词： Data mining

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Energy-efficient routing protocols for WSNs: A comparative analysis 2nd

Energy-efficient routing protocols for WSNs: A comparative a...

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2nd International Conference on Smart Computing and Informatics, SCI 2018

作者： Mohan, B.A. Sarojadevi, H. Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India

ISBN: (纸本)9789811319204

Cluster-based routing protocols are used to reduce the energy consumption of sensor nodes, which involves them in short-distance communication to cluster head than to sink which is located far away. Cluster head collects and aggregates data to decrease number of transmissions to sink. As cluster head performs multitask, energy is extensively used, which in turn leads to premature death. To avoid this, cluster head role is shifted among the member nodes in the cluster. A slight modification is to introduce mobile nodes to relay information from cluster heads to sink to reduce the energy consumption of the cluster heads. In this paper, comparative analysis of reactive protocols (LEACH and LEACH-C), single mobile node protocol (NDCM), and multiple mobile node-based hybrid protocol (HADMMN) is carried out. Use of multiple mobile nodes significantly improves the performance of the network. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Energy utilization

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A Novel Machine Learning-Based Artificial Voice Box

A Novel Machine Learning-Based Artificial Voice Box

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： Naveen Kumar Priya Nandihal Madhumala R B Piyush Kumar Pareek Nikshepa T Sowmya S R Department of Computer Applications (BCA) Dr NSAM First Grade College (Affiliated to Bangalore City University) Bengaluru Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Bangalore

ISBN: (纸本)9781665493970

Patients may experience a great deal of discomfort while undergoing rigorous medical procedures for the identification of vocal abnormalities. As a result, there has been a lot of interest in automated speech recognition and disorder detection approaches in recent years, and these methods have shown to be effective. Voice recordings have been acquired from the Saarbruecken Voice Database for the purpose of this study. The signals undergo preprocessing using Hybrid Wiener Filter Discrete Wavelet Transforms in order to de-noise and eliminate any silence that may have been there (HWFDWT). Cat Swarm Optimization is used to extract features, and Mel Frequency Cepstrum Coefficients are taken into account (CSOMFCC). Classification using Modified Optimized Back Propagation Network Disorder voice Classification is then used to sort the features in the end (MOBPNDC). In terms of Accuracy, Precision, Recall, F-Measure, and Time period, the classification scheme beats the current Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) approaches. The neural speech system is a gadget that enables individuals who are unable to talk to communicate their thoughts and emotions with the outside world. It is a piece of equipment that can record the electric pulses that are generated by the brain and turn them into a synthetic voice. - Provide an overview of the concept or solution that you want to build. The electrical activity of the brain will be recorded and then sent into a synthesiser. The Synthesizer will convert the signal into voice when it has finished decoding it. The voice that has been deciphered is then supplied to an artificial voice box. The brain's electrical activity is used to generate an artificial voice, which is then output via the box.

关键词： Support vector machines Backpropagation Wiener filters Synthesizers Transforms Speech recognition Feature extraction

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Thefted vehicle identification system and smart ambulance system in VANETs 5th

Thefted vehicle identification system and smart ambulance sy...

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5th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2018

作者： Nagaraja, S.R. Nalini, N. Mohan, B.A. Pasha, Afroz Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811360008

VANETs act as crucial component of intelligent transportation system (ITS). VANETs are capable of providing connectionless communication between mobile nodes (vehicles) and static nodes such as RSU and BSU to enhance safety and comfort of vehicles on the highways or in urban environments. There is no system in place for finding stolen vehicles and providing faster movement of ambulances on heavy traffic lanes. In this paper, we discuss about automated system for traffic management such as for finding stolen vehicles and providing faster movement of ambulances on heavy traffic lanes, and we are coming up with two systems namely thefted vehicle identification system (TVIS) and smart ambulance system (SAS), respectively. © Springer Nature Singapore Pte Ltd 2019.

关键词： Vehicular ad hoc networks

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Apple Fruit leaf Disease Detection and Classification using Quantum Behaved Particle Swarm Optimization

Apple Fruit leaf Disease Detection and Classification using ...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gayatri Math Vishwanath P Myasar Mundher Adnan R Archana Reddy S. meenakshi Sundaram Department of ECE H.K.E. Society's S.M.V. College of Engineering Raichur Visvesvaraya Technological University Belgaum Karnataka India Department of ECE H.K.E. Society's S.M.V. College of Engineering Raichur Karnataka India The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India

Detecting and classifying apple fruit leaf diseases manually is a time-consuming and expensive task, requiring expertise. This process not only affects the quality of the leaf disease image but also demands expert intervention. In the dataset, there are 1615 healthy images distributed among four classes: apple scab, apple rust, apple black rot, and healthy apple. Data augmentation techniques were involved image inversion, noise injection, colour enhancement, rotation and scaling. Despite these efforts, the existing model faces challenges in an inefficient search space with high dimensions and local optima. To enhance the Quantum-Behaved Particle Swarm Optimization (QBPSO) based Long Term-Short Memory (LSTM) dimension the search space is expanded globally, independently detecting the leaf disease. Feature extraction is performed using Squeeze Net layers to analyse the leaf disease, reducing parameters to improve the detection process. The fire modulus in the analysis is involved in the process. The hyperparameter tuning process utilizes QBPSO to independently detect and classify the leaf disease, with the Schrödinger equation playing a crucial role in the process. The obtained results demonstrate that the proposed method achieves better accuracy of 98.76%, Precision of 98.72%, Sensitivity of 98.82% and F1-score of 98.70%. on the plant dataset. These results ensure detection and classification performance compared to other existing methods, such as Discrete Wavelet Transformation (DWT), Butterfly Optimization Algorithm (BOA) and Chaotic Salp Swarm Algorithm (CSSA).

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Ant Colony Optimization based Medical Image Preservation and Segmentation

Ant Colony Optimization based Medical Image Preservation and...

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Advanced Technologies in Intelligent Control, Environment, Computing & Communication engineering (ICATIECE), International Conference on

作者： Priya Nandihal Piyush Kumar Pareek Victor Hugo Costa De Albuquerque Madhumala R B Ashish Khanna V Sunil Kumar Dept of Computer Science and Design Dayananda Sagar Academy of Technology and Management Bangalore Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Department of Teleinformatics Engineering Federal University of Ceara Brazil Department of ISE Dayananda Sagar Academy of Technology and Management Bangalore CSE Dept Maharaja Agrasen Institute of Technology GGSIPU Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru

ISBN: (纸本)9781665493970

Image segmentation is an essential component in many different types of computer vision systems. Image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work.

关键词： Integrated optics Image segmentation Ant colony optimization Biomedical optical imaging Optical design Image edge detection Metaheuristics

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Quantifying the classification of exoplanets: In search for the right habitability metric

arXiv

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arXiv 2021年

作者： Safonova, Margarita Mathur, Archana Basak, Suryoday Bora, Kakoli Agrawal, Surbhi Indian Institute of Astrophysics Bangalore India Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering Pennsylvania State University United States Department of Information Science and Engineering PES University South Campus Bangalore India Department of Computer Science and Engineering IIIT Pune India

What is habitability? Can we quantify it? What do we mean under the term habitable or potentially habitable planet? With estimates of the number of planets in our Galaxy alone running into billions, possibly a number greater than the number of stars, it is high time to start characterizing them, sorting them into classes/types just like stars, to better understand their formation paths, their properties and, ultimately, their ability to beget or sustain life. After all, we do have life thriving on one of these billions of planets, why not on others? Which planets are better suited for life and which ones are definitely not worth spending expensive telescope time on? We need to find sort of quick assessment score, a metric, using which we can make a list of promising planets and dedicate our efforts to them. Exoplanetary habitability is a transdisciplinary subject integrating astrophysics, astrobiology, planetary science, even terrestrial environmental sciences. It became a challenging problem in astroinformatics, an emerging area in computational astronomy. Here, we review the existing metrics of habitability and the new classification schemes (machine learning (ML), neural networks, activation functions) of extrasolar planets, and provide an exposition of the use of computational intelligence techniques to evaluate habitability scores and to automate the process of classification of exoplanets. We examine how solving convex optimization techniques, as in computing new metrics such as aCDHS and bCEESA, cross-validates ML-based classification of exoplanets. Despite the recent criticism of exoplanetary habitability ranking, we are sure that this field has to continue and evolve to use all available machinery of astroinformatics, artificial intelligence (AI) and machine learning. It might actually develop into a sort of same scale as stellar types in astronomy, to be used as a quick tool of screening exoplanets in important characteristics in search for potentially hab

关键词： Extrasolar planets

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Human Activity Recognition with Anomaly Prediction for E-Health Systems using Lightweight AI

Human Activity Recognition with Anomaly Prediction for E-Hea...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： R. Priyanka Pramila Anita Gehlot Berdjouh Chafik Ajith Sundaram Lekha H Finney Daniel Shadrach Naveen I G R.M.K Engineering College Gummidipoondi India Uttaranchal University Dehradun India Departement of Computer Science University of ElOued Algeria Amrita School of Business Amrita Vishwa Vidyapeetham Kochi India Adi Shankara Business School Department of ECE KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Computing devices should really be mindful of human actions in order for flourishing human-assistive systems to be used effectively in medical services and human-robot collaboration operations. It is necessary to create a trustworthy real-time action recognition mechanism for the uninterrupted and error-free functioning of such smart devices. This can only be done with simple, intelligent methods that make advantage of all-around sensors. With both the connection of time series information in mind, this work presents a fresh way to information architecture for improved extracting features. Using the assistance of a specially created iOS application, the movement data were gathered using a smartphone. In order to identify significant both spatial and temporal elements from the signals, data from eight operations were structured into single and double channels. The Fourier and wavelet domains were used to depict raw data alongside the time domain. Other CNN models that were employed to meet the deep-learning classification of the tasks fared worse than a deep neural network that utilized a double-channeled time-domain input. Several public datasets were used to further examine this strategy, and improved results were obtained. Ultimately, the learned model's applicability was evaluated in real time on a PC and a smartphone, yielding encouraging results.

关键词： Training Deep learning Neural networks Time series analysis Medical services Feature extraction Real-time systems

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