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1st International Conference on Computational Intelligence and Network Systems, CINS 2023

作者： Veningston, K. Venkateswara Rao, P.V. Pravallika Devi, M. Pranitha Reddy, S. Ronalda, M. Department of Computer Science and Engineering National Institute of Technology Srinagar 190006 Jammu and Kashmir Srinagar India Department of Computer Science and Engineering GITAM School of Technology Andhra Pradesh Visakhapatnam530045 India

ISBN: (纸本)9783031489839

Proteins play an important role in the human body and in plants. A lack of expertise in protein labeling in plants can make it extremely difficult to characterize and comprehend the precise roles and activities of different proteins. Furthermore, it restricts development in fields like biotechnology, disease resistance, and crop enhancement. The presented project focuses on plant protein classification, aiming to overcome the challenges arising from limited protein labeling knowledge. Advanced machine learning techniques, including various classification algorithms such as Logistic Regression, Decision Tree, K-nearest neighbors (KNN), Support Vector Machines (SVM), Random Forest (RF), Multinomial Naive Bayes (NB), AdaBoost, and XGBoost, are employed to accurately classify protein sequences into their respective families. This classification approach provides valuable insights into the functions and roles of proteins within plants, ultimately advancing our understanding of plant biology. This attempt offers new possibilities for advancement in critical sectors such as agriculture, drug discovery, and genomic research by eliminating the limitations associated with limited protein labeling knowledge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Proteins

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Combinatorial Kinetic Dualistic Auction Model for Metaverse Service Allocation 1

Combinatorial Kinetic Dualistic Auction Model for Metaverse ...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Singh, Raj Sarkar, Pritam Bandyopadhyay, Anjan Swain, Sujata Sardar, Tanvir Habib Pandey, Bishwajeet School of Computer Engineering KIIT 751024 India Department of Computer Science GITAM University Bengaluru India Department of Intelligent System and Cybersecurity Astana IT University Kazakhstan

ISBN: (纸本)9798350364378

The focus of this research work revolves around the utilization of a specialized"Combinatorial Kinetic Dualistic Auction Model" designed specifically for"metaverse Services." The main goal is to improve the allocation of resources in this ever-changing environment and enhance the overall experience for end-users. Through the exploration of metaverse services, we aim to further develop the comprehension and practical application of the Combinatorial Kinetic Dualistic Auction Model, ultimately contributing to a more immersive and responsive virtual experience. ©2024 IEEE |

关键词： Resource allocation

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Lightweight Cryptographic Approach to Address the Security Issues in Intelligent Applications: A Survey

Lightweight Cryptographic Approach to Address the Security I...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Rajesh, S.M. Prabha, R. Gitam Deemed to Be University Department of Computer Science Engineering Bengaluru India Dr. Ambedkar Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474511

With the origin of the Internet of Things (IoT),massive real-time applications are evolving, but in this cyber world, securing the data of those devices or users is to be focussed more on. Intelligent services are the base components of IoT applications, such as intelligent systems, smart homes, smart cities, smart cars, Etc. An unfold challenge is securing the large amount of data generated and transmitted over the network in the IoT ecosystem by resisting cyber-attacks and adopting the efficient cryptographic method for a resource-constrained environment. This paper focuses on the exhaustive work carried out on user/device identity and a lightweight cryptographic viable solution for the IoT ecosystem through ECC (Elliptic-curve cryptography). The study assures the secure transmission of data and authentication of device/user using various security strategies of ECC for real-time IoT applications. A systematic survey of the previous works is conducted to determine the critical aspects of designing ECC-based authenticated and secured transmission solutions for the IoT ecosystem. The outcome of this study is essential components of ECC, requirements, and solution architecture of ECC for various security aspects in the IoT ecosystem to ensure user/device privacy and secured data transmission and its challenges are addressed. © 2023 IEEE.

关键词： Authentication

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A Wavelet Transform Algorithm based Detection and Classification of Sleep Apnea for Monitoring of Health

A Wavelet Transform Algorithm based Detection and Classifica...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Pillai, Beena G Madhurya, A.J. Babu, V Jeevan Kumar Reddy, A Siva Siddiqua, Ayesha Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

One-third of a person's life is spent sleeping, and this physiological phenomenon serves several purposes, such as restoring and maintaining normal brain metabolism, allowing the cardiovascular system to recharge, and restoring metabolic balance to the body's glucose supply. Sleep has intimate ties to physical restoration, hormone regulation, and immune system maintenance. During sleep, the body's metabolic rates shift from catabolism (the breakdown of tissues) to anabolism (the rebuilding of tissues). This state of mind is not a constant slumber but rather a dynamic internal structure with a periodic temporal progression. It was often believed that sleep was a non-active state, but modern research has shown that our brains are actually quite busy while we sleep. A multivariate Wavelet transform-based feature selection method has been used since different authors have advocated differing usefulness of different features from the time domain, the frequency domain, and nonlinear features in earlier work on sleep (SL) staging. This provided an optimal collection of 21 features that led to significantly better sleep stage classification performance than previous approaches. Once the signals in the time domain have been transformed into the time-frequency domain, the wavelet coherence coefficients can be calculated to determine the instantaneous respiration rate, and the instantaneous power of the individual signals, the common power, and the phase difference can be examined at the corresponding frequency component. The suggested method improves upon earlier approaches in terms of both event detection and categorization, as evidenced by the experiments. © 2023 IEEE.

关键词： Tissue

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Trend Analysis of Groundwater Levels in Visakhapatnam Coastal Aquifer 1

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26th International Conference on Hydraulics, Water Resources and Coastal engineering, HYDRO 2021

作者： Priyanka, V.M. Ramesh, M. Srinivas, Y. Department of Civil Engineering GST GITAM Deemed to be University Visakhapatnam530045 India Department of Computer Science and Engineering GST GITAM Deemed to be University Visakhapatnam530045 India

ISBN: (数字)9789811985249

ISBN: (纸本)9789811985232

Groundwater exploitation has been increasing day by day due to the population increase, and overexploitation advances to severe groundwater depletion. This paper examines the trend analysis and spatiotemporal disparity for groundwater levels (GWL) in three Mandals, i.e., Bheemunipatnam, Visakhapatnam rural, and Visakhapatnam urban areas in Visakhapatnam district for the period from 2008 to 2020. The data of GWL in piezometric wells and open wells are used to predict the GWL trend present in the study area. For spatial analysis, the interpolation technique of inverse distance weighted is applied to develop the GWL maps. Mann–Kendall test and Sen’s slope estimator were considered to predict the trend present in the GWL. From the exploration of the analysis, it is reflected that there is an increasing trend in GWL in some locations in the study area. Bheemunipatnam, Venkatapuram, Sivajipalem, Madhuravada, Chukkavanipalem, and YSR park are the significant places where groundwater is declining. Increasing depth trends to GWL, especially near the coast, can cause a decline in water quality and may lead to seawater intrusion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aquifers

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm Driven by Machine Learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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A stacking ensemble approach for identification of informative tweets on twitter data

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International Journal of Information Technology (Singapore) 2023年第5期15卷 2651-2662页

作者： Dasari, Siva Krishna Gorla, Srinivas Prasad Reddy, P.V.G.D. Department of Computer Science and Engineering GMR Institute of Technology GMRIT Rajam and Research Scholar Andhra University Visakhapatnam India Department of Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India

In the Present era, social media information plays an impact on our daily activities. Accurate media information identification is also challenging because of fake or spam information. Social media may receive this information via Twitter, Facebook, Youtube, LinkedIn, etc. In the context of natural disasters, people share their opinions via social media. Identifying such tweets which are informative or non-informative via these social media is a challenging issue. This work focuses on when a natural disaster happens and how social media information has been categorized, such as informative or non-informative. In this paper, we collected seven datasets from various disaster events from crisisNLP and categorized the tweet information using the stacking ensemble approach. In the proposed work, we considered ensemble stacking approach, in which for base learner we chosen the top five best-performed machine learning models where as for meta-learner we chosen the logistic regression. We tested our stacking model performance with matrices, namely accuracy, precision, recall, and F1 score. We also compared our approach results with other state-of-the-art models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： CrisisNLP embedding Machine Learning Stacking Tweets Vectorization

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of COVID-19 Using SIS-CODE Algorithm

Detection of COVID-19 Using SIS-CODE Algorithm

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International Conference on Business Intelligence and Information Technology, BIIT 2022

作者： Kim, Daegeon Govathoti, Sudeepthi Department of Architecture Engineering Dongseo University Busan Korea Republic of Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India

ISBN: (纸本)9789819934157

The COVID-19 pathological test can be carried out with a sample taken from the nose, the back of the throat, or both, using a special swab. Currently, laboratories are checking through a process called Polymerase Chain Reaction (PCR) on a collected sample from the suspected person. PCR is used to find genetic material on the virus. PCR amplifies the genetic material such that the virus can be detected easily. In general, the PCR process is used to match the genetic material with the virus that causes COVID-19 is unique. Blood tests are also can be done to detect if a person has previously been exposed to the virus or not. This blood test is useful for knowing if a person may have developed immunity. In our paper, we propose a unique technique which will work on the microscopic image of Coronavirus affected tissue and detects the tissue is affected or damaged by the Coronavirus or not. In this research, we use the microscopic image(s) of a smear of epithelial throat/bronchial tract tissue. Firstly, image smoothing is conducted, and then Region of Interest (ROI) is detected. Then that 24-bit colour image (ROI) is converted to grey to bi-colour images using edge detection by setting a threshold. At last, the contours is detected and counted. In our technique, the resultant contours are the affected areas, and the count is the number of possible affected areas, an approximation. Thus, the infection depth can be guessed. Our technique is not only detecting the infection but also identifies the approximate number of attacked spots in a specific tissue area. Thus, the proposed technique identifies the person has COVID-19 or Coronavirus disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Detection and Identification of Lung Cancer Using an Improvised CNN Model: A Novel Approach to Assist Doctors in Diagnosing Lung Cancer

Detection and Identification of Lung Cancer Using an Improvi...

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International Conference on Business Intelligence and Information Technology, BIIT 2022

作者： Mohammed, Sabah Govathoti, Sudeepthi Satyanarayana, K.V. Joshua, Eali Stephen Neal Department of Computer Science Lakehead University 955 Oliver Road Thunder BayONP7B 5E1 Canada Department of CSE GITAM School of Technology GITAM Deemed-to-be-University Telangana Hyderabad India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science Engineering GITAM School of Technology GITAM Deemed-to-be-University Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9789819934157

Conditions that affect the lungs and make breathing difficult are respiratory ailments. Lung cancer is a disease that affects people all over the globe. The greater the number of individuals aware of them, the better. The likelihood of surviving lung cancer jumps from 14 to 49% if the disease is detected early. Even though CT scans are far more accurate than X-rays, a comprehensive diagnosis requires several scans. The development and testing of a deep neural network for identifying lung cancer in CT images are carried out here. The CNN, DenseNet software was used to determine if the lung image was normal or malignant. This was accomplished via the use of adaptive boosting. There are 201 photographs of lungs in the research collection. Only 15% of the pictures are chosen for testing and classification, while the rest are discarded. According to the testing results, the proposed approach is 95.85% accurate. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computerized tomography

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