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International Conference on Inventive Computation Technologies (ICICT)

作者： Srinivasa Reddy Seelam Charitha Sai Thivari Bhagath Parabattina Narendra Kumar G Sumanth K Dept. of Computer Science and Engineering Lakireddy Bali Reddy College of Engineering Mylavaram India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation’s economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome.

关键词： Computational modeling Neural networks Estimation computer architecture Production Soil Predictive models

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Hybrid RNN-CNN model for predicting stock market trends

Hybrid RNN-CNN model for predicting stock market trends

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Bhanujyothi H C I Jeena Jacob Dept. of Computer Science and Engineering GITAM Deemed to be University Bengaluru India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Stock market prediction has become a challenging task in today’s world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model.

关键词： Accuracy Computational modeling Time series analysis Predictive models Market research Data models Nonlinear dynamical systems Convolutional neural networks Stock markets Long short term memory

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“Liver Disease Risk Prediction Using Regularized Deep Learning: A Novel Approach”

“Liver Disease Risk Prediction Using Regularized Deep Learn...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Parvez Rahi Sandeep Singh Kang Ajay Pal Singh Inderjeet Singh Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy_Liver, Low_Risk, Moderate_Risk, High_Risk, and Severe_Liver_Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model’s scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings.

关键词： Green energy Accuracy Liver diseases Computational modeling Soft sensors Scalability Medical services Predictive models Data models Physiology

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Automated Cognate Detection as a Supervised Link Prediction Task with Cognate Transformer

arXiv

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arXiv 2024年

作者： Mahesh Akavarapu, V.S.D.S. Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound correspondences, proto-language reconstruction, phylogenetic classification, etc. Previous state-of-the-art methods for cognate identification are mostly based on distributions of phonemes computed across multilingual wordlists and make little use of the cognacy labels that define links among cognate clusters. In this paper, we present a transformer-based architecture inspired by computational biology for the task of automated cognate detection. Beyond a certain amount of supervision, this method performs better than the existing methods, and shows steady improvement with further increase in supervision, thereby proving the efficacy of utilizing the labeled information. We also demonstrate that accepting multiple sequence alignments as input and having an end-to-end architecture with link prediction head saves much computation time while simultaneously yielding superior performance. © 2024, CC BY.

关键词： Automation

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MusicoNet: A Social Network for Musicians Based on the Internet of Musical Things and People Paradigm

MusicoNet: A Social Network for Musicians Based on the Inter...

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Internet of Sounds, International Symposium on the

作者： Luca Turchet Jacopo Tomelleri Alessandro Ruffo Dept. Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350366525

ISBN: (纸本)9798350366532

To date, there are a number of online social networks dedicated to musicians. However, these do not truly leverage and capitalize on the musical diversity, i.e., the variability that exists across musicians, their instruments, musical activities and social relations. For instance, existing social networks are not designed to search and find for musicians with specific characteristics related to their profile and, above all, the actual particuliarities of their playing style. More importantly, their integration with the Internet of Musical Things (IoMusT) has been largely overlooked thus far. To bridge these gaps, in this paper we propose MusicoNet, a social network for musicians based on IoMusT technologies. The network leverages Semantic Web methods and is made accessible through an app for smartphones and tablet devices, which can wirelessly interact with smart musical instruments (or, through a laptop, with conventional instruments). MusicoNet was not conceived to exchange topic-oriented communication through textual and photographic posts as it occurs in popular social networks, but to support the search for and connectivity among musicians having given diversity factors. Such a search is not simply based on sole textual queries as it occurs in conventional social networks, but also on content-based queries which can be performed via musical instruments. We describe the technical implementation of MusicoNet, the IoMusT ecosystem it enables, and a preliminary technical validation. We then discuss the lessons learned and future avenues for the proposed technology, which represents the first instance of the recently proposed Internet of Musical Things and People paradigm.

关键词： Semantic Web Bridges Portable computers Social networking (online) Instruments Ecosystems Music Cultural differences Smart phones

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Gait Recognition: A Cross - Dataset Evaluation of Deep Learning Models

Gait Recognition: A Cross - Dataset Evaluation of Deep Learn...

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

作者： Hemal Singloo Manoj Kumar Simran Namdev Kinsukh Suni Sonbawne Nishant Indoria Dept. Computer Science and Engineering VIT Bhopal University Sehore India

This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications.

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“RNN-Driven Prognosis of Heart Disease Based on Health Parameters”

“RNN-Driven Prognosis of Heart Disease Based on Health Para...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Parvez Rahi Inderjeet Singh Ajay Pal Singh Alok Kumar Dept. of Computer Science & Engineering Chandigarh University Mohali Punjab India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Cardiovascular diseases (CVD) are the leading cause of death globally and are estimated to affect 17. 9 million deaths annually. Early diagnosis is very important if one is to prevent adverse consequences. However, traditional diagnostic methods can take time to complete and miss subtle trends in patient information. This research presents an innovative approach utilizing a Recurrent Neural Network (RNN) model, optimized through Stochastic Gradient Descent (SGD), for the prediction of seven distinct types of heart disease: CAD, Heart Failure, AFib, Hypertensive Heart Disease, Congenital Heart Disease, Cardiomyopathy, and PAD. Their architecture is specifically suited for direct modeling of sequential patterns of the health related data that in turn helps to detect temporal patterns and changes in them. This model use ReLU activation function in their hidden layers, dropout in preventing overfitting and batch normalization for stable learning. Moreover, the feature augmentation is used in the model making it use various psychological and physiological parameters that help in making comprehensive predictions on the risks of heart disease. After training the RNN with a Kaggle dataset and properly tuning it, the model obtained an accuracy level of 91%, which means that is highly useful in identifying risk factors of cardiovascular diseases and could be used for early diagnosis and risk factors identification.

关键词： Heart Training Solid modeling Recurrent neural networks Stochastic processes Psychology Predictive models Cardiovascular diseases Batch normalization Tuning

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Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks

Unveiling Predictive Factors in Apple Quality: Leveraging LI...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Rituparna Chowdhury Rudra Das Fowzia Binta Faruk Ananna Arnob Saha Sadia Nawar Md. Hamid Hosen Dept. of Computer Science & Engineering East Delta University Chittagong Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies.

关键词： Adaptation models Machine learning algorithms Biological system modeling Static VAr compensators Artificial neural networks Boosting Vectors

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Multivariate Analysis of Urban Air Pollution: Clustering and Patterns Across Major Asian Cities

Multivariate Analysis of Urban Air Pollution: Clustering and...

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Future Machine Learning and Data science (FMLDS), IEEE International Conference on

作者： Anika Rahman Mst. Taskia Khatun Dept. of Computer Science & Engineering Stamford University Bangladesh Dhaka Bangladesh

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis and evaluate the effectiveness of six clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, and Spectral Clustering. Performance was assessed using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, WCSS, Cohesion, and Separation. The novelty of this work lies in the comparative analysis of these clustering methods on air pollution data, providing new insights into pollution dynamics across Asian cities. The analysis identified four distinct clusters- ‘High Pollution’, ‘Moderate Pollution’, ‘Ozone-Dominated Pollution’, and ‘Low Pollution’- with K-Means proving to be the most effective. Significant disparities were found, particularly in South and East Asia, where countries like India, China, and Pakistan exhibited the highest pollution levels. Additionally, an examination of capital cities revealed specific pollution patterns and the primary pollutants-PM2.5, NO2, CO, and Ozone-aiding in identifying sources and affected regions. These findings underscore the need for targeted regional pollution control strategies.

关键词： Gases Environmentally friendly manufacturing techniques Asia Urban areas Pollution control Clustering algorithms Transportation Air pollution Indexes Public healthcare

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Work in Progress: Enhancing Students' Learning Experience in Formal Methods through Practical Assignments

Work in Progress: Enhancing Students' Learning Experience in...

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IEEE World engineering Education Conference (EDUNINE)

作者： Ruth Schorr Dept. of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt Germany

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on passing formal methods courses with minimal effort. Performance in compulsory formal methods courses is usually below average, with students perceiving the subject as overly mathematical and lacking practical relevance. To address these challenges and enrich the learning experience, we have integrated mandatory group homework assignments into our teaching framework. Students are required to work collaboratively on case studies and present their solutions during class. This work-in-progress paper provides an experience report on enhancing the learning possibilities of master’s students in a model checking course at the Frankfurt University of Applied sciences (FRA-UAS).

关键词： Model checking engineering education

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