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data-driven decision Support by Utilizing Machine learning to Predict Passenger Flow for Route and Station Optimization

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Journal of The Institution of Engineers (India): Series B 2025年 1-20页

作者： Patel, Madhuri Patel, Samir B. Swain, debabrata Department of Computer Engineering Pandit Deendayal Energy University Gandhinagar India Department of Information Technology L D College of Engineering Ahmedabad India

A smart public transportation system with reliable services addresses urban challenges like traffic congestion, infrastructure maintenance, travel costs, and pollution. As part of smart city initiatives, urban public transport systems have adopted advanced data collection methods, enabling data analytics and predictive model development. This study evaluates optimal machine learning models for predicting passenger flow across routes and stations within the Thane Municipal Transport (TMT) network using real historical data from March 2021 to May 2022. Key spatial and temporal features such as weekday patterns, route distance and direction, public holidays, COVId-19 impacts, and weather conditions were identified through exploratory data analysis and incorporated into the models. Several algorithms, including decision Trees, XGBoost, Extra Tree Regressor, and Random Forest, were implemented to predict daily passenger commute on an hourly basis. Random Forest emerged as the most accurate model, achieving an MAE of 114.55, RMSE of 236.71, and an R2 of 0.9516, leveraging its ensemble approach to capture complex, non-linear patterns and ensure robustness to variability, including disruptions caused by the pandemic. These findings provide actionable insights for optimizing route design, bus allocation, maintenance scheduling, and overall transport system efficiency, enabling stakeholders to achieve sustainable and adaptive public transport management. © The Institution of Engineers (India) 2025.

关键词： Traffic congestion

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Influence of cohesion on California bearing ratio of clay-gravel mixtures

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International Journal of Sediment Research 2023年第3期38卷 374-386页

作者： Rajesh Jain Ketan Timani Manish Pandey Civil Engineering Department L.D.College of Engineering Vishwakarma Government Engineering College Department of Civil Engineering National Institute of Technology

An understanding of the behavior of cohesive sediment is required to solve various engineering problems such as scour around bridge elements, mitigation of soil erosion, pavement design, river bed degradation,stable channel design. Pavement foundation designers principally use the California bearing ratio(CBR)to describe the subgrade and subbase materials and their strength. Several laboratory experiments were done to study the variation in the CBR of cohesive mixtures comprised of clay-gravel mixtures. Nine different clay-gravel mixtures were used in which the clay content varies from 10% to 50% by *** variation of the CBR with clay percentage, moisture content, and undrained shear strength parameters was studied. The CBR value reduces with the increase in the moisture content and clay fraction in the mixtures and increases with an increase in the dry density of the mixture under unsoaked conditions. The CBR also increases with the increase of the angle of internal friction of clay-gravel mixtures.A functional relation has been identified to estimate the CBR of clay-gravel mixtures. Using multiple linear regression analysis(MlRA), a relation is proposed to estimate the CBR of clay-gravel mixtures under unsoaked conditions. A statistical analysis was done to judge the behavior of the pertinent variables on the CBR. The proposed relation predicts the CBR of clay-gravel mixtures very well. Artificial neural network(ANN) analysis using R programming was also done to determine the effects of the pertinent variables on the CBR. ANN methodology was applied to predict the contribution of each variable. Three different methods: Garson algorithm, Olden algorithm, and lek's profile model are used to assess the influence of variable parameters. The Olden algorithm and lek's profile both show positive association of cohesion with CBR in an unsoaked condition. The role of moisture content was found to be marginally negative in the Olden algorithm and lek's profile results

关键词： Clay–gravel mixture Cohesive sediment California bearing ratio Clay percentage Sediment properties

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Insights into Ocular disease Recognition: A Machine learning Approach 3

Insights into Ocular Disease Recognition: A Machine Learning...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Paul, Sosmita Bhatt, Geet dey, Aditya Sarvajanik College of Engineering and Technology Be in Electronics and Communication Engineering Gujarat Surat India Be in Computer Engineering L D College of Engineering Gujarat Ahmedabad India

ISBN: (纸本)9798350375190

This study reviews the potential benefits of machine learning techniques in diagnosing ocular disorders, aiming to address the limitations of traditional diagnostic methods. Various Machine learning (Ml) algorithms, including Support Vector Machine, decision Tree, K-Nearest Neighbor, Random Forest, Naïve Bayes, and Extra Trees, are compared to determine the effectiveness of each algorithm in identifying eye diseases. Through analysis and testing, the study concludes that Extra Trees (ET) has achieved the highest accuracy rate of 97%. This finding suggests that ET could be the most suitable option for enhancing the accuracy and efficiency of eye disease detection systems. Overall, these research discoveries contribute to the ongoing efforts to improve diagnostic tools for ocular health, highlighting the potential of Machine learning (Ml) in transforming healthcare. © 2024 IEEE.

关键词： decision trees

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A Semisupervised learning Approach for Code Smell detection

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SN computer Science 2025年第2期6卷 1-21页

作者： Kheria, Ishita Gada, dhruv Karani, Ruhina Computer Engineering Department D.J. Sanghvi College of Engineering Mumbai India

detecting code smells early in the development process is crucial for ensuring system longevity and performance. This paper proposes a semisupervised learning approach to detect code smells using both widely used labeled and manually collected unlabeled data. The approach addresses the limited availability of labeled data and improves generalizability. The effectiveness of the proposed method is demonstrated through a clustering-based approach for class-level and a label propagation-based approach for method-level code smell detection. The data collection process involves gathering a diverse set of code samples from peers, ensuring a diverse range of software projects and coding styles. A comparative analysis is performed against traditional supervised algorithms to evaluate the performance of the semisupervised approach. Compared with traditional supervised learning approaches and past studies, the recommended semisupervised learning method resulted in accuracy increases of up to 8.4% for class-level and method-level code smells. The proposed approach has the potential to enhance code quality, maintainability, and software development processes, overcoming the challenges posed by the scarcity of labeled data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte ltd. 2025.

关键词： Accuracy Code smells Pseudo-labelling Semi supervised learning

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different Respiratory lung Sounds Prediction using deep learning 4

Different Respiratory Lung Sounds Prediction using Deep Lear...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vasava, Rajeshree Parsingbhai Joshiara, Hetal A. L D College of Engineering Computer Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9798350300093

The sounds produced by the lungs when breathing might provide important information to physicians. Based on the findings, a deep learning-based approach is recommended for the prediction of breathing-related lung sounds. The Proposed model was trained in lung sounds collected from people suffering from a broad variety of respiratory conditions. The research improves classifying lung sounds, by audio to image spectrogram features is taken and used to train a deep convolutional neural network. The proposed technique accurately predicts many different types of respiratory lung sounds, demonstrating the promise of deep learning in this domain. This research results have important implications for the development of automated diagnostic tools that might help doctors make correct diagnoses of respiratory disorders more quickly and accurately. © 2023 IEEE.

关键词： Convolution

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and Applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;lGXP and GlCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes lSTM and AMl-CNN models. © The Author(s), under exclusive licence to Springer Science+Business Media, llC, part of Springer Nature 2024.

关键词： Textures

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Panoramic study of hydrodynamic cavitation and its applications in wastewater treatment

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Environmental Technology Reviews 2025年第1期14卷 371-399页

作者： Pandya, Hiral Parikh, Sachin Banka, Samidha Chemical Engineering Department L. D. College of Engineering Gujarat Technological University Ahmedabad India Chemical Engineering Gujarat Technological University Ahmedabad India

The development of sustainable and effective wastewater treatment technology is imperative due to the growing demand for clean water. While successful, conventional procedures frequently struggle with complex organic contaminants and necessitate large chemical inputs. The novel technique known as hydrodynamic cavitation (HC) presents a potent and environmentally acceptable method for treating wastewater. Wastewater containing complex organic compounds is ideally broken down by hydrodynamic cavitation (HC). Chemicals utilized in many industrial processes, including as dyes, solvents, insecticides, and other organic compounds, are efficiently broken down by HC. It targets numerous compounds, hormones, and antibiotics found in pharmaceuticals and personal hygiene items. Cleaning products such as emulsifiers and detergents are broken down by HC effectively. Bacteria, viruses, and other pathogens in wastewater can be efficiently eliminated by the extremely reactive free radicals produced during the collapse of HC bubbles. When combined with AOPs (like H₂O₂, ozone, Fenton’s reagent), this effect is magnified, resulting in a higher concentration of radicals available for pollutant degradation. Hydrodynamic cavitation (HC), when applied through novel reactor designs, enhances pollutant degradation by optimizing cavitation conditions and improving energy efficiency. This makes the HC approach a promising disinfection technique. In establishments that process food, HC can be used for cleanliness and disinfection. These facts are reviewed by the authors in the given script thoroughly. © 2025 Informa UK limited, trading as Taylor & Francis Group.

关键词： disinfection

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Blackspot Identification and Hazards on Urban Road: A Case Study of S. G. Highway, Ahmedabad 9th

Blackspot Identification and Hazards on Urban Road: A Case S...

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9th Conference on Transportation Systems engineering and Management, CTSEM 2023

作者： Shekh, Faraj Singh, Parmar Vijay Shukla, Rena N. Branch L. D. College of Engineering Ahmedabad India Department of Civil Engineering L. D. College of Engineering Ahmedabad India

ISBN: (纸本)9789819760749

According to MoRTH (Ministry of Road and Transport Highways, Bharat), in Bharat, were recorded 1,53,972 persons killed by road accidents only in the year 2021. At least 1 out of 10 people killed on roads across the world are from Bharat. Globally, Bharat ranks second in terms of number of road accidents and ranks first in the number of persons killed per lakh people. Traffic injuries are not equally spread over the world, however;some countries are hit harder than others, and the chance of being killed in a road crash depends on where one lives. The paper explores critical factors influencing road safety and proposes effective strategies for mitigating accidents and fatalities. It analyzes the multifaceted nature of road safety, incorporating elements such as probable causes, severity index, and blackspots. By examining statistical data, the paper underscores the importance of comprehensive road safety measures. The paper identifies major blackspots and a three-year consolidated severity index. Additionally, it addresses the significance of public awareness campaigns, law enforcement, and policy development in fostering a safer road environment. Sarkhej-Gandhinagar highway is taken as a case study. The findings highlight the need for a holistic approach that integrates technological advancements with behavioral interventions. Ultimately, this paper contributes to a deeper understanding of the intricacies involved in road safety and provides valuable insights for policymakers, researchers, and stakeholders striving to reduce road accidents and safeguard human lives. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2025.

关键词： Highway accidents

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Analyzing Online Purchase Behaviour Through Machine learning Approach Based Ordering Mechanism 14

Analyzing Online Purchase Behaviour Through Machine Learning...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sheth, Rutvi S. Thaker, Chirag S. L. D. College of Engineering Computer Engineering Department Ahmedabad India

ISBN: (纸本)9798350335095

In recent years, the rise of user-generated content has significantly influenced consumer behavior in online shopping. The analysis of product-related data, particularly sentiments expressed by consumers on e-commerce platforms, has garnered considerable attention. It is crucial to examine questions related to various aspects to assist sellers in making informed decisions and maximizing their profits. While it is feasible to sort purchasers into pre-buy or post-buy stages in light of procurement data, there are situations where purchasers might have gained the item from an outside source or on the other hand assuming the buy was made by another relative. To tackle this challenge, a novel text classification method has been proposed in this study that employs an ordering mechanism to determine whether a text was posted before purchase (pre-purchase) or after purchase (post-purchase). The approach presented in this study combines the strengths of transfer learning and sequential learning techniques within a hybrid deep learning model. This integration leads to improved performance of the proposed model, BBRCA, when compared to other machine learning models, as evidenced by higher accuracy, precision, recall, and F1-score. © 2023 IEEE.

关键词： Consumer behavior

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Electronic health records protection strategy by using blockchain approach

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Multimedia Tools and Applications 2024年第39期83卷 86883-86894页

作者： Kathole, Atul B. Patil, Sonali d. Kumbhare, Savita Vhatkar, Kapil Kshirsagar, Aniruddha P. Kimbahune, Vinod V. Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India IT Dept. Pimpri Chinchwad College of Engineering Pune India Computer Science and Engineering Department K.B.P College of Engineering Satara Satara India

Electronic health records (EHRs) provide significant challenges for medical organizations due to the digitalization of traditional medical information. When using EHRs, both doctors and patients are accustomed to spending considerable time conducting the necessary queries;nevertheless, the acquired data is not always related to the sought one, and access may be revoked if this is the case. The exchange and maintenance of medical health records are among the most crucial tasks in the healthcare system. The integrity of health records is crucial, as is maintaining patient anonymity, and either would have devastating effects in the event of a breach. Therefore, it is one of the most important reasons to keep electronic health records safe. Modern healthcare infrastructure is notoriously difficult to use, wasteful, and fraught with problems related to data integrity, security, and privacy. Better EHR management and monitoring, however, has the potential to lessen the severity of the problems with security and complexity. The blockchain's potential, with its decentralized and trustworthy nature in the healthcare industry, makes this a reality. Problems with data management and the way information is validated and disseminated are at the heart of the healthcare delivery system's inherent difficulties. Improved access to medication tracking, hospital assets, drug systems, patient information, and so on is the primary benefit of employing blockchain technology in healthcare data management. Because of how important it is to have access to a patient's medical history when prescribing medication, blockchain technology has the potential to significantly enhance the current healthcare delivery system. Therefore, it is critical to develop a blockchain-based system for verifying and protecting medical records. The primary objective of this article is to create a permissioned blockchain-based Ciphertext Policy-Attribute-based encryption system to protect privacy and regulate acces

关键词： Blockchain

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