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

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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Expediting Prediction Accuracy with Exploration and Incorporation of Virtual Data

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SN computer science 2024年第5期5卷 545页

作者： Nayak, Sarat Chandra Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science and Engineering School of Technology GITAM (Deemed to be University) Hyderabad India Department of Computer Science Fakir Mohan University Odisha Balasore 756019 India Department of Computer Science Yonsei University 50 Yonsei-ro Sudaemoon-gu Seoul 03722 South Korea

Achieving high accuracy in time series forecasting (TSF) is challenging;particularly for series with inadequate training data or insignificant correlation among data. Modelling such time series with ANN based systems which are data driven and necessitate abundant training samples, leads to poor generalization ability thus, inferior accuracy. In such circumstances, the time series may be enriched with few more virtual data points (VDPs) explored from existing training samples keeping the corelation factor in mind. Incorporation of such VDPs intensify the volume of training data and helps the model in extracting the inherent nonlinearity efficiently. This article proposes an extreme learning with Rao algorithm based VDP (ELRA-VDP) generation and incorporation approach to expediate TSF accuracy of ANN model. Extreme learning method (ELM) considers random hidden node parameters and apply a generalized inverse function on hidden node outputs to determines the output node parameters. Inclusion of random hidden node parameters may reduce the performance of ELM therefore, we used Rao algorithm (RA) to optimize the initial parameters and construct an efficient learning scheme, i.e., ELRA. Given a training sample, the ELRA estimates the VDPs as well as forecasts the next data in an alternative manner. The parameter-free optimization mechanism of RA and lower complexity with faster learning ability of ELM makes ELRA computationally efficient. TSF problems are then conducted with and without VDPs. Simulation studies show that ELRA-VDP method yields better predictions than others with a lowest MAPE value of 0.1746 and quite beneficial for datasets having a lesser amount of data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Extreme learning machine Interpolation Rao algorithm Time series forecasting Virtual data position

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An Effective Model for Smartphone Based Pothole Classification and Admin Alerting System

An Effective Model for Smartphone Based Pothole Classificati...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Vaddi, Snehit Maddi, Vishnu Vipul Sai Krishna Ramineni, Yoga Chalamalasetti, Lalitha Sri Gitam Deemed University Department of Computer Science Engineering Visakhapatnam India Shri Vishnu Engineering College for Women Department of Computer Science Engineering Bhimavaram India

ISBN: (纸本)9798350322309

This research analyses and compares machine learning and deep learning models for classifying pothole images and recording exact GPS coordinates using smartphones' built-in sensors. This deep learning classification algorithm uses convolutional neural networks (CNN) built with the TensorFlow library. Five distinct deep learning and machine learning models were trained and compared (SVM, Random Forest, Decision trees, KNN, InceptionResnetV2, InceptionV3). The most accurate model has a 91 percent accuracy rate. According to the results of this study, the suggested approach is reliable and inexpensive. © 2023 IEEE.

关键词： Smartphones

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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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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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Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

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Intelligent Automation & Soft Computing 2023年第3期35卷 2773-2787页

作者： Amal H.Alharbi S.Karthick K.Venkatachalam Mohamed Abouhawwash Doaa Sami Khafaga Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Electrical Electronics and Communication Engineering GITAM School of TechnologyGITAM Deemed to be UniversityBengaluru CampusKarnataka560065India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end *** numerous security tech-niques,face recognition is considered a high-security *** fuse and carry out face identiﬁcation as an access authority into these ***,face identiﬁcation authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized *** the existing spooﬁng detection algorithm,there was some loss in the recreation of *** research proposes an unobtrusive technique to detect face spooﬁng attacks that apply a single frame of the sequenced set of frames to overcome the above-said *** research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused ***,this pro-posed method is tested with the Cross-ethnicity Face Anti-spooﬁng(CASIA),Fetal alcohol spectrum disorders(FASD)*** database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video *** images are taken with three different quality cameras:low,average,and high-quality real and spoofed *** extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

关键词： Image processing edge detection edge net auto-encoder face authentication digital security

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Generation of Electricity by using Waste Materials 4

Generation of Electricity by using Waste Materials

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Patnana, Naresh Jyothi, Pothumahanty Sai Chappa, Anilkumar Changalasetty, Yoga Yashaswi Sri Varshit, Tummalapalli Sai Anagha Aishwarya Varma, P. Department of Electrical Electronics and Communication Engineering Gitam University Andhra Pradesh Visakhapatnam India Andhra Pradesh Visakhapatnam India Andhra Pradesh Tadepalligudem India Department of Computer Science and Engineering Gitam University Andhra Pradesh Visakhapatnam India Gitam University Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798331528614

The growing population and rising standards of living have led to the need for additional solid waste management. It has become evident that if waste is not handled efficiently, it will ultimately become unusable. This highlights the importance of proper solid waste management, as municipal waste materials have harmful effects on the environment. Any material that isn't in liquid or gaseous form is considered solid waste. This category includes various types of waste such as sewage sludge, municipal, industrial, agricultural, and mechanical waste. Solid waste can also take many forms, including pathological, industrial, agricultural waste, and general debris. Organic waste like meat, fruits, and vegetables, which contain moisture, is classified as solid waste during energy production. The main aim of this research is to generate electricity from waste materials, with a key focus on reducing carbon emissions. Energy is stored and utilized through circuits powered by materials like plastic, rubber, and general waste, with batteries serving as storage units. The project demonstrates the effective reduction of pollution by using LED bulb filters during energy generation. Ultimately, the study successfully illustrates how electricity can be generated from waste and stored in rechargeable batteries for future use. ©2024 IEEE.

关键词： Municipal solid waste

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN computer science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Sharma, Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering.design limitations. On the other hand, metaheuristic approaches promise to find global optima in real-world optimization problems, even in nonlinear and multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, whereas SBO finds the best cluster heads by considering communication range. By comparing the suggested approach to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster performance, simplifies cluster head selection in WSNs, and increases network efficiency overall, according to the results. To prove its efficacy in enhancing network performance, the suggested solution outperforms standalone SBO and CSA approaches across a range of performance parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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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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Evaluating Ensemble Models on Imbalanced Data Sets: A Comparative Study across Varied Minority Class Ratios 4

Evaluating Ensemble Models on Imbalanced Data Sets: A Compar...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Surajit Nayak, Samaleswari P. Sahoo, Biswajit Nayak, Sarat Chandra Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India Gitam Deemed to Be University School of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349856

The complexities of using ensemble models to rectify class imbalance in healthcare datasets are investigated in this study. Using well-known methods such as Random Forest, Gradient Boosting, AdaBoost, CatBoost, LightGBM, XGBoost, BRF and EE we perform an extensive analysis on data-sets that exhibit different levels of class imbalance. The study uses stratified k-fold cross-validation for robust evaluation and the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation. ROC curves are shown, and performance indicators like as accuracy, precision, recall, F1-score, and area under the curve (AUC) are examined. The results shed light on the complex effects of ensemble models on unbalanced healthcare data-sets and offer directions for further investigation. © 2024 IEEE.

关键词： Adaptive boosting

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