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6th International Conference on Harmony Search, Soft Computing and Applications, ICHSA 2020

作者： Jha, Preeti Tiwari, Aruna Bharill, Neha Ratnaparkhe, Milind Nagendra, Neha Mounika, Mukkamalla Department of Computer Science and Engineering Indian Institute of Technology Indore Indore India Department of Computer Science and Engineering Mahindra University Ecole Centrale School of Engineering Hyderabad India Biotechnology ICAR-Indian Institute of Soybean Research Indore Indore India

ISBN: (纸本)9789811586026

In this paper, we propose a novel Kernelized Scalable Random Sampling with Iterative Optimization Fuzzy c-Means (KSRSIO-FCM) and a Kernelized Scalable Literal Fuzzy c-Means (KSLFCM) clustering algorithms for big data framework. The evolution of kernelized clustering algorithms led us to deal with the nonlinear separable problems by applying kernel Radial Basis Functions (RBF) which map the input data space nonlinearly into a high-dimensional feature space. The experimental result shows that the KSRSIO-FCM algorithm achieves significant improvement in terms of F-score, Adjusted Rand Index (ARI), and Normalized Mutual Information (NMI) for Big Data. Experimentation is performed on well-known IRIS datasets to show the effectiveness of proposed KSRSIO-FCM in comparison with KSLFCM. The KSRSIO-FCM implemented on Apache Spark shows better potential for Big Data clustering. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Big data

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Text-Driven Voice Conversion via Latent State-Space Modeling

arXiv

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arXiv 2025年

作者： Li, Wen Martinez, Sofia Shah, Priyanka Department of Computer Science George Mason University United States Institute of AI Research Rochester Institute of Technology United States Department of Electrical Engineering Indian Institute of Technology Indore India

Text-driven voice conversion allows customization of speaker characteristics and prosodic elements using textual descriptions. However, most existing methods rely heavily on direct text-to-speech training, limiting their flexibility in controlling nuanced style elements or timbral features. In this paper, we propose a novel Latent State-Space approach for text-driven voice conversion (LSS-VC). Our method treats each utterance as an evolving dynamical system in a continuous latent space. Drawing inspiration from mamba, which introduced a state-space model for efficient text-driven image style transfer, we adapt a loosely related methodology for voice style transformation. Specifically, we learn a voice latent manifold where style and content can be manipulated independently by textual style prompts. We propose an adaptive cross-modal fusion mechanism to inject style information into the voice latent representation, enabling interpretable and fine-grained control over speaker identity, speaking rate, and emphasis. Extensive experiments show that our approach significantly outperforms recent baselines in both subjective and objective quality metrics, while offering smoother transitions between styles, reduced artifacts, and more precise text-based style control. Copyright © 2025, The Authors. All rights reserved.

关键词： State space methods

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Deep learning for brain age estimation: A systematic review

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Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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GAN, CNN and ELM Based Breast Cancer Detection

GAN, CNN and ELM Based Breast Cancer Detection

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Innovation in technology (INOCON), IEEE International Conference for

作者： Vandana Kate R Ushasree R.M. Tharsanee Mrs. Taranjeet Kukreja Swarnlata Saraf Bhoopathy Varadharajan Acropolis Institute of Technology and Research Indore Madhya Pradesh India Dayananda Sagar Academy of Technology and Management Bangalore Karnataka India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode Tamilnadu India University Institute of Pharmacy Pandit Ravishankar Shukla University Raipur Chhattisgarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Secunderabad Telangana India

The most prevalent kind of cancer among females is known as breast cancer. One of the most significant challenges facing modern medicine is improving the accuracy of breast cancer screenings. In addition to lowering costs associated with treatment, accurate diagnosis of cancer, regardless of whether it is benign or malignant, is of utmost importance. Image processing is a tool that can detect breast cancer in its early stages without causing any damage. The processing of images comes with a number of inherent uncertainties that can be caused by a variety of factors, including the sampling to noise ratio, the initial digitalization, the intensity, and the special domain. We put the Extreme Learning Machine (ELM) to work classifying, identifying, and instructing. This research suggests a GAN-CNN-ELM-based technique for detecting breast cancer. This method produces an accuracy of about 99% which outperforms the GAN-CNN model.

关键词： Technological innovation Uncertainty Costs Extreme learning machines Image processing Generative adversarial networks Breast cancer

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High-Performance Computing based Scalable Online Fuzzy Clustering Algorithms for Big Data

High-Performance Computing based Scalable Online Fuzzy Clust...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Preeti Jha Aruna Tiwari Neha Bharill Milind Ratnaparkhe Om Prakash Patel Rapolu Pulakitha Aditi Chauhan Computer Science and Engineering Indian Institute of Technology Indore Indore India Computer Science and Engineering Mahindra University Hyderabad India Biotechnology ICAR-Indian Institute of Soybean Research Indore India

ISBN: (纸本)9781665487696

With the rise of big data trends so quickly, real-time stream data processing has become very important. Stream data is a type of big, fast, and unreliable dataset that cannot be handled well by traditional algorithms. Designing the algorithm that can efficiently process streaming data is a challenging task. This paper shows how important is to make a real-time clustering algorithm for data streams with high concept drift and an algorithm that can adapt to different dimensions. We propose Scalable Random Sampling Online Optimization Weighted Fuzzy c-Means (SRSOO-WFCM) algorithms for handling Big Data in a High-Performance Computing (HPC) environment using an Apache Spark cluster. To compare SRSOO-WFCM with the traditional Online Fuzzy c-Means (OFCM) algorithm, we made a scalable version of OFCM named SOFCM. The proposed SRSOO-WFCM and SOFCM are incremental algorithms that involve making one sequential run through the data subsets. We employ both loadable and very large datasets to perform extensive experiments that facilitate comparing the proposed SRSOO-WFCM and SOFCM algorithms. The proposed SRSOO-WFCM algorithm performs better than the SOFCM in terms of Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and F-score, respectively.

关键词： High performance computing Clustering algorithms Big Data Market research Data processing Real-time systems Indexes

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SMS Spam Detection using Advance Naive-Bayes Approach

SMS Spam Detection using Advance Naive-Bayes Approach

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Yojna Arora Neha Gupta Yogesh Singh Rathore Nidhi Bansal Aina Mehta Shikha Chadha Ashwani Kumar Department of Computer Science and Engineering Sharda University Greater Noida India School of Computer Science & Information Technology Symbiosis University of Applied Sciences Indore Madhya Pradesh India Department of Computer Science and Applications School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Computer Science & Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies (Deemed to be University) Faridabad Haryana India School of Computer Science Engineering and Technology Bennett University India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Recently, with increased use of mobile phones, it has transformed into a multibillion-dollar Short Message Service or SMS. However, the drop in the cost of messaging services has led to an increased number of unsolicited commercial messages, or spam, being delivered to mobile phones. Several reasons exist for which traditional algorithms used in email filtering may not efficiently classify the spam in SMS such as the absence of strong SMS spam databases, with the short length and features of the message, the informal language used, etc. This paper uses a real SMS spam database After preprocessing the data and extraction of relevant features, Naïve Bayes machine learning techniques is applied to get the results. Then it is analyzed using confusion matrix, accuracy, precision and recall values. A learning curve with clearly defined relationship between the training and testing data set and overall error at the end.

关键词： Training Machine learning algorithms Databases Unsolicited e-mail Message services Feature extraction Mobile handsets Tokenization Bayes methods Testing

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An Intelligent and Cost-Effective IoT-Based Irrigation System Using Machine Learning 2

An Intelligent and Cost-Effective IoT-Based Irrigation Syste...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kadiyala, Sreedevi Potluri, Chandra Srinivas Birke, Ali Hussen Mehra, Aashish Rajput, Sneha Padmavathi, Katakam Guru Nanak Institutions Technical Campus Department of Computer Science and Engineering Hyderabad India Werabe University Department of Information Technology Werabe Ethiopia Werabe University Department of Information Systems Werabe Ethiopia School of Retail Management Symbiosis University of Applied Sciences Indore India Prestige Institute of Management and Research Gwalior India Stanley College of Engineering and Technology for Women Department of Electronics and Communication Hyderabad India

ISBN: (纸本)9798350371055

This work presents and implements a low-cost irrigation system for smart agriculture that is based on the Internet of Things (IoT). In order to continuously monitor environmental data in real time, the system is equipped with a network of sensors, including pressure, temperature, moisture, and water level sensors. In order to anticipate when irrigation pumps will switch on, machine learning techniques including Artificial Neural Networks (ANN), Decision Trees (DT), Naive Bayes (NB), and Support Vector Machines (SVM) are linked depending on established parameters. The study shows that the ANN model can identify complicated patterns in the agricultural environment with an accuracy of up to 98.33%. Farmers are able to make well-informed decisions quickly thanks to the cloud connectivity and intuitive interface of remote monitoring and control. Because predictive modeling minimizes pump activation delays, it lessens the chance of both over- and under-irrigation. The suggested strategy makes the most use of available water and provides opportunities for precision farming, which is a major step forward for sustainable agriculture. The study's findings demonstrate how well the system uses resources and open the door for the future creation of innovative, scalable agricultural technology. © 2024 IEEE.

关键词： Agriculture IoT Machine Learning Precision Farming Water Management

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Cognitive Computing’s Role in Shaping the Future Landscape of Healthcare

Cognitive Computing’s Role in Shaping the Future Landscape ...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Anunay Jain Neelesh Jain Ashish Verma Anupama Jain Department of Information Technology Shri G. S. Institute of Technology Indore India Department of Computer Science Engineering Trinity Institute of Technology and Research Bhopal India School of Mathematical & Physical Sciences Dr. Harisingh Gour Central University Sagar Madhya Pradesh India Department of Computer Science Engineering Sagar Institute of Research & Technology Bhopal India

As the healthcare industry undergoes a paradigm shift towards data-driven and patient-centric models, cognitive computing emerges as a transformative force. This research paper delves into the profound impact of cognitive computing on the future landscape of healthcare. Through an extensive review of literature, case studies and emerging trends, this paper explores the applications, benefits, challenges, and ethical considerations associated with integrating cognitive computing into healthcare practices. Drawing on interdisciplinary perspectives, we examine how cognitive computing enhances diagnostic precision, optimizes treatment plans, facilitates personalized medicine, and improves overall healthcare delivery. The paper also scrutinizes the ethical implications of leveraging cognitive computing in healthcare, considering issues such as data privacy, bias, and the human-machine interface. By synthesizing existing knowledge and proposing future research directions, this paper contributes to the ongoing discourse on the role of cognitive computing in shaping the trajectory of healthcare.

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Towards Analyzing Mobile App Characteristics for Mobile Software Development

Towards Analyzing Mobile App Characteristics for Mobile Soft...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Anil Patidar Ugrasen Suman Acropolis Institute of Technology & Research Indore India School of Computer Science & Information Technology Devi Ahilya Vishwavidyalaya Indore India

A mobile application (i.e., mobile app) is a small software program, which is mainly developed for mobile phones. Mobile apps have some peculiar characteristics, which are concerned with particular aspects in the form of requirements, pertaining to hardware, software and network connectivity. Designing an appropriate mobile app according to users' perspective depends on the mobile app characteristics. In this paper, we present a review of various available mobile app characteristics concerning to present mobile apps. It is observed that the user experience is one of the most exciting and evolving feature that attracts the majority of mobile app users. In order to deal with user experience, we have further explored and found some essential factors that affect it. Furthermore, mobile app paradigms play an important role for mobile app development. Mobile app characteristics help to select a suitable paradigm for mobile apps development. We have analyzed that hybrid mobile app paradigm fits in most of the mobile app development situations.

关键词： Analytical models Computational modeling Software User experience Mobile handsets Hardware Mobile applications

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Soil Analysis and Classification to Improve the Crop Yield in Agriculture Using Fast Graph - CRNN Approach

Soil Analysis and Classification to Improve the Crop Yield i...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Kathiravan, Mathur Nadarajan Bande, Shivangi Tiwari, Sadhana Koshariya, Ashok Kumar Manju, M. Suganthi, D. Dr N. G. P Arts and Science College Department of Biotechnology Tamil Nadu Coimbatore India Institute of Engineering & Technology Madhya Pradesh Indore India Prestige Institute of Engineering Management Research Iet Davv Madhya Pradesh Indore India Collage of Agriculture and Research Station Indira Gandhi Krishi Vishwavidyalaya Department of Plant Pathology Fingeshwar Chhattisgarh Gariyaband India School of Agriculture Sr University Telangana Warangal India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Chennai Tamilnadu Thandalam India

ISBN: (纸本)9798350300857

Changes in land use over the past few decades have left their mark on Earth's surface. Key ecosystem services provided by soils are currently being altered due to global environmental changes. In light of these factors, monitoring equipment is required to maintain a healthy ecological status and advance soil conservation on a local, regional, and global scale. This system demonstrates the potential utility of remote sensing in collecting soil properties. Methodologies based on various remote sensing devices and classification strategies have been developed for assessing soil characteristics. Soil analysis by remote sensing is a relatively new area of study in the agricultural sciences. The majority of people in India rely on agriculture as a means of subsistence and economic survival. The low cost of yield prediction has led to a decline in agricultural output in recent years. Predicting yields is challenging because of the many variables at play, including as soil, rainfall, and fertilizers. The quality of the soil is crucial for agricultural output. MSC and SNV are used in the suggested method for preprocessing. LDA is used for feature selection. The proposed method employs FGCRNN to train the envisioned model. The proposed model is evaluated in comparison to the CNN and GRU models. Its performance is superior to that of the other two types. © 2023 IEEE.

关键词： Remote sensing

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