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Spectral and Graph Based Deep Learning Models for Schizophrenia Detection 1

Spectral and Graph Based Deep Learning Models for Schizophre...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Nair, Chirag Sana, Ameen Mehvish Saha, Satyam Sumathi, D. VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350359756

Deep learning has intervened in the field of neuroscience by inspecting activities in the brain through Electroencephalogram (EEG) signals. This research investigates the application of two unconventional preprocessing approaches to EEG signals, aiming to enhance the classification of schizophrenic adolescents from healthy individuals. To analyze the EEG signals, the researchers used three different mathematical techniques called Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and Laplace Transform. These techniques helped to extract important features from the signals, or to identify patterns in the brain activity that could be related to schizophrenia. After extracting the features, the researchers then used them as input for different deep learning models to detect schizophrenia. Deep learning models like VGG-16, Variational AutoEncoders, ChronoNet and GraphCNN were used. It was found that the STFT processed signals on VGG-16 gave the best results. Certain challenges like variation in data, the need for the large annotated datasets along with the interpretability of deep learning models were encountered. To address these issues, this study deployed preprocessing techniques and the domain knowledge has been integrated to enhance the model robustness and generalizability. © 2023 IEEE.

关键词： Laplace transforms

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Parameter Investigation Study On Task Scheduling in Cloud Computing 12

Parameter Investigation Study On Task Scheduling in Cloud Co...

引用

12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Santhosh Kumar, M. Karri, Ganesh Reddy VIT-AP University School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350318210

The term parameter investigation study is to describe the process of examining different factors that have an effect on the efficiency of work scheduling algorithms in cloud computing. Some examples of such factors are the amount of time allotted for each work, the number of available processors, the amount of resources being used, the amount of communication overhead, and the deadline limitations. For optimal resource utilisation and timely completion of tasks, cloud computing relies heavily on task scheduling. Since metaheuristic optimisation algorithms can quickly and efficiently identify near-optimal solutions in huge and complex problem spaces, they have become a popular method for scheduling tasks on the cloud. In this study, we examine how metaheuristic optimisation algorithms can be used to schedule tasks in the cloud. In addition, a fair research was conducted on a wide range of cloud computing task scheduling parameters. © 2023 IEEE.

关键词： Cloud computing

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AEFA-Enhanced Design for Optimized FODD 3

AEFA-Enhanced Design for Optimized FODD

引用

3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Nayak, Chandan Suha, K.B.A. School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravati522237 India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798350349207

In this article, an efficient approach for designing fractional order digital differentiators (FODDs) of both second and fifth orders using the powerful meta heuristic Artificial Electric Field Algorithm (AEF A) is proposed. AEFA simulates the fundamental principles of electrostatic force, as described by Coulomb's law, and the laws of motion established by Newton. The performance metric used for evaluating the quality of designed FODDs is the Mean Absolute Relative Magnitude Error (MARME). The experimental results highlight the exceptional efficacy of AEFA in addressing complex FODD design problems, with the obtained solutions significantly outperforming existing cutting-edge methods. © 2024 IEEE.

关键词： Optimization

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Feature level Fine Grained Sentiment Analysis for Classifying Online Restaurant Reviews 2

Feature level Fine Grained Sentiment Analysis for Classifyin...

引用

2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Revathi, Krosuri Lakshmi Satish, Aravapalli Rama Rao, Popuri Srinivasa School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9798350397635

In recent years, the popularity of social networking sites has skyrocketed. Social media allows individuals to express their opinions on almost every topic. These opinions take variety of ways, such as tweets, Instagram posts, blogs, Facebook posts, and so on. The process of describing and categorizing the opinions represented in the form of post, document or comment is known as sentiment analysis. The goal of sentiment analysis is typically to determine the attitude of customers toward a good or service. Customer's feedback is critical for organizations and social media that can serve as a promising tool to enhance and improve business opportunities if reviews have been evaluated on social media in a timely manner. As a result, the purpose of this study is to examine online restaurant reviews given by customers. The proposed research makes two contributions. First, it conducts sentiment analysis (SA) and categorizes each and every comment given in the text into five labels i.e.;positive, negative, neutral, highly positive and highly negative. Second, comments are automatically characterized using text categorization techniques based on the feedback from the customers about taste, service, ambiance, food, and value for money. For training and testing, dataset available from Kaggle was used. For classification, numerous algorithms were employed, such as the Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes Classifier (NB). This method evaluates the performance of these algorithms and random forest algorithm produced the best results, with highest accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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Real time DNN-based Face Mask Detection System using MobileNetV2 and ResNet50 6

Real time DNN-based Face Mask Detection System using MobileN...

引用

6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Divya Meena, S. Siri, Chinta Sai Lakshmi, Paruchuri Sindhura Doondı, Nalukurthı Sheena Sheela, J. School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9798350398496

Coronavirus has changed the entire world. Studies indicate that to minimize the spread of the virus it is advisable to use masks for maximizing safety and keeping the community safe by slowing down the spread of the coronavirus. However, it becomes tedious and un-feasible to manually check each and every person who wears a mask or not. In that regard, technology presents digital and innovative solutions for this complex problem. This research study proposes a novel face mask detection algorithm. For face mask detection, Real Face Mask Detection dataset which consists of 4095 images in which mask images are 2165 and without mask 1930 images are taken to train and test the model using various pre-trained algorithms like (VGG19) besides proposing 2 different algorithms. The other models compared and tested are ResNet50, and MobileNetV2. These models are trained and tested in a Google Collaboratory environment with the help of TensorFlow and Keras software. A comparative study is made between these algorithms to decide which is the one that is the most suitable algorithm for the environment based on different parameters. The final model is applied to random images to check the accuracy of the model. © 2023 IEEE.

关键词： Deep learning

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An Expert Uncertainty in Healthcare Using Materialized View through Big-Query 3

An Expert Uncertainty in Healthcare Using Materialized View ...

引用

3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Srinivasarao, Popuri Satish, Aravapalli Rama Revathi, Krosuri Lakshmi School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (纸本)9781665494007

One of the greatest data-generating industries is the health care sector, thus we must prepare the way for improved technology implementation that appeals to users. This method uses materialised views to develop adaptive queries and identify connectivity based on terms found in the search criteria. This method helps the user understand the steps to take when querying the technology to find out more information. In order to construct a model that meets user requirements for users to understand the condition of the medical medication analysis and as well as treatment analysis, the fields of health and the materialised views using Google's big query can be of assistance. An accurate way to evaluate how well a technique is understood will be created by analysing drug-related data and disease symptoms. To establish better health practises, a user or patient must do the following. The OLap technique is an outdated method of comprehending the data aggregation, and other models, such as business intelligence and ETL (Extract, Transform, Load), will aid the model in comprehending the inner feelings of the keywords that are being used in the implementation of health care. The optimization method employing Big Query can strengthen the model to comprehend the data's insights. © 2023 IEEE.

关键词： Big data

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Sentiment Classification of Low Resource Language Tweets Using Machine Learning Algorithms 2

Sentiment Classification of Low Resource Language Tweets Usi...

引用

2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Chattu, Kannaiah Sumathi, D. School of Computer Science and Engineering VIT-AP University Vijayawada India

ISBN: (纸本)9798350347579

Sentiment analysis is a way of figuring out if a piece of writing or a sentence is positive or negative. Over the past few years, it has garnered a significant amount of interest in the English language. As a result of the rapid expansion of social media in the modern day, an increasing amount of content can be found in languages apart from English. Telugu represents one of the many native languages for which there is comparatively little information on social media. Finding labeled data of sentences in Telugu for sentiment analysis is becoming increasingly challenging. This study provides Sentikanna, a standard Telugu speech corpus built to assist Telugu Sentiment Analysis across multiple domains, including Recipe, Tourism, and Movie reviews. Sentikanna is a corpus including preprocessed and manually annotated Telugu sentences from a variety of sources, annotated according to annotation rules. All the scripts written in the Telugu language have been used. apart from the creation of the corpora, polarities have been assigned to the sentences. In total, 3113 Reviews are annotated that have been extracted from various domains. Proposed dataset is used to evaluate different sentiment analysis methods and its performances. Later, sentiment evaluation algorithms using support vector machines (SVM) and Naive Bayes (NB) were applied to the proposed dataset in addition to Sentirama data samples. Performance evaluation has been done and it has been observed that SVM and NB showed promising results on proposed dataset comparatively. © 2023 IEEE.

关键词： Sentiment analysis

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Biomarkers for Early Detection of Pancreatic Cancer: A Review

Biomarkers for Early Detection of Pancreatic Cancer: A Revie...

引用

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Dodda, Koteswaramma Muneeswari, G. VIT-AP University School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350312126

Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient's condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions. © 2023 IEEE.

关键词： Biomarkers

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Optimizing Cost Estimation Using YOLOv9 5

Optimizing Cost Estimation Using YOLOv9

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ibrahim, S.P. Siddique Rohith, Sanagapalli Lakshman Nikhilesh, Pulluru Gayatri, Bonagiri Krishna Hasmithanjana, Kanuri School of Computer Science and Engg. VIT-AP University AP Amaravati India

ISBN: (纸本)9798331518097

Accurate wall measurements are essential for esti- mating painting costs, yet traditional methods rely on manual tools such as ladders and tape measures, making the process error-prone and potentially hazardous. This research study proposes an AI-driven approach for automating wall measurement and cost estimation through images captured by a mobile device. Leveraging YOLOv9, a cutting-edge object detection model, the proposed method identifies large surfaces, such as walls, with enhanced speed and accuracy over previous YOLO versions. By calculating wall area directly from detected dimensions, this approach translates measurements into cost estimates using standardized painting rates. This automated system not only minimizes manual effort but also improves measurement accu- racy and safety by eliminating physical tools. Real-world testing demonstrates the model's effectiveness in providing accurate wall detection and cost prediction, offering a practical solution for both homeowners and painting professionals aiming to streamline estimation and planning processes. © 2024 IEEE.

关键词： Cost benefit analysis

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Plant Diseases Detection Using Transfer Learning 1

引用

4th International Conference on computer and Communication Technologies, IC3T 2022

作者： Divya Meena, S. Kumar, Katakam Ananth Yasodharan Mandava, Devendra Bhavya Sri, Kanneganti Panda, Lopamudra Sheela, J. School of Computer Science and Engineering VIT-AP University Amaravati India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

For several decades, plants have demonstrated to be an effective assessment of human life in a variety of fields. Plant pathogens are currently wreaking havoc on our farming sector. As an outcome, farmers are expected to make a loss. The precise and rapid diagnosis of plant diseases can aid in the development of an early treatment method, reducing enormous economic pain. Manually detecting plant diseases necessitates specialist knowledge of plant diseases, which is difficult, time consuming, and exhausting. In this research, a profound technique for detecting and classifying plant diseases from leaf images captured at multiple resolutions is provided. The inclusion of deep learning networks on top of basic models for effective feature learning is the main goal of this study. Various leaves image datasets from different countries are used to train a dense deep neural networks architecture. Because there were not enough photos, the gathered image data was augmented. In this experiment, 70,874 data shots were utilized to fit the classifier and 17,856 data images have been used to analyze it. The suggested CNN architecture can accurately categorize miscellaneous varieties of plant leaves, according to experimental evidence, and provide the possible remedies. In terms of accuracy, precision, recall, and F-score, a comparison of MobileNetV1 and ResNet34 structure was conducted, and the results revealed that the ResNet34 model is an efficient approach for disease categorization. The results of the experiments show that on photographs with complicated backgrounds, an average test accuracy of 98.91% may be achieved. The processing time is 0.067 s with considerable precision for an individual plant leaf image, indicating that it is both real time and practicable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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