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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Vardhan, Senapathi Vishnu Balaji, Killemsetty Viswa Sai Kumar, C. Ashok Kumar, C. Jothi School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research investigates a hybrid approach for predicting movie revenue by integrating machine learning models with sentiment analysis. The growing influence of social media and online discussions offers a valuable opportunity to understand audience engagement and sentiment, which are critical in forecasting box office performance. By analyzing data from various social media platforms alongside movie metadata from sources like IMDb and TMDb, this study seeks to create a comprehensive model that can predict a movie's opening weekend revenue and classify it as a 'Hit,' 'Average,' or 'Flop.' The work highlights the value of audience sentiment, pre-release engagement, and historical box office trends in predicting financial outcomes. By using a combination of quantitative metrics (likes, shares, views) and qualitative data (sentiment scores, discussion themes), the study aims to refine revenue predictions and improve classification accuracy. The hybrid model's ability to incorporate both traditional and real-time data offers several advantages, including better responsiveness to audience perceptions, the ability to identify emerging trends, and more accurate revenue forecasts. This research provides actionable insights that can aid movie studios, marketers, and investors in decision-making processes, ultimately reducing financial risks associated with movie releases. The findings contribute to the growing field of predictive analytics in the entertainment industry, offering a novel and data-driven approach to understanding and forecasting movie success. © 2025 IEEE.

关键词： Predictive analytics

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

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Alexandria Engineering Journal 2025年 126卷 116-130页

作者： Ragab, Mahmoud Ashary, Ehab Bahaudien Kateb, Faris Hakeem, Abeer Mosli, Rayan Albogami, Nasser N. Nooh, Sameer Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Electrical and Computer Engineering Department Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Information Systems Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques. © 2025 The Authors

关键词： Convolutional neural networks

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Smart Environmental Monitoring System for Air and Water Quality Index

Smart Environmental Monitoring System for Air and Water Qual...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Tripathi, Ansh Dubry, Saransh Rama, P. Angayarkanni, V. Kalaivani, J. Srm Institute of Science and Technology Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology TN Kattankulathur603203 India Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai603203 India Srmist Kattankulathur Faculty of Engineering and Technology Department of Computing Technologies India

ISBN: (纸本)9798331529833

The quality of air and water is essential for human health, environmental sustainability, and ecosystem balance. However, pollution from industrial activities, urbanization, and agricultural practices has made effective environmental monitoring a global priority. Traditional monitoring methods, which rely on manual sampling and laboratory analysis, are often time-consuming and lack real-time capabilities. This paper presents a Smart Environmental Monitoring (SEM) system leveraging IoT and sensor technology to monitor air and water quality in real time using two key sensors: the MQ135 for Air Quality Index (AQI) and the pH sensor for Water Quality Index (WQI). By focusing on these two critical parameters, the SEM system offers a streamlined yet robust solution for assessing environmental quality. The system collects data continuously, transmits it to a cloud platform for processing, and employs machine learning algorithms to forecast future pollution trends. A user-friendly dashboard displays real-time AQI and WQI levels, historical trends, and predictive insights, empowering users to respond proactively to environmental risks. Deployment results demonstrate the SEM system's capability in detecting pollution patterns, identifying high-risk areas, and predicting pollution spikes during peak traffic and in industrial zones. Comparative analysis with traditional monitoring highlights the SEM system's superior accuracy, scalability, and predictive abilities. Although the SEM system has higher initial setup costs, its operational efficiency and long-term benefits in mitigating environmental risks make it a sustainable choice for public health and environmental management. © 2025 IEEE.

关键词： Environmental monitoring

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Adaptive sail fish optimizer based optimized region selection for robust video steganography

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International Journal of Information technology (Singapore) 2025年第4期17卷 2417-2427页

作者： Babu, J. Suresh Niranjana, G. Ramana, Kadiyala Department of Computing Science & Engineering School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chengalpattu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology (Autonomous) Hyderabad India

Securing data transmission in a digital era is a difficult one due to the broad application of the Internet, personal computers, and mobile phones for communication. Traditional video steganography techniques sometimes fail to identify the best pixels for encoding hidden data, resulting in lowerquality and robustness. To solve this issue, this article proposes an innovative steganography technique that improves the security and quality of data hiding. The proposed method takes cover video and secret images as input. The Structural Similarity Index Measurement (SSIM) first collects key frames from the input video. The adaptive sailfish optimizer (ASFO) is then used to find the best regions within these keyframes to insert the secret data in. To improve security, the discrete wavelet transform (DWT) is applied to the specified region, and the LL band is used for the hiding process. The secret image is encrypted by Adaptive elliptical curve cryptography (AECC) and transformed to binary bits, which then get embedded in the chosen band. This method leads to the construction of a stego-video. The extraction procedure is repeated using the same approach, which ensures secure and robust data transmission. Performance metrics, including PSNR, BER, NAE, TAF, and NC are analyzed to compare each method's efficacy. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive elliptical curve cryptography And stego-video Discrete wavelet transform Structural similarity index measurement Video steganography

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Stepwise monogamous pairing genetic algorithm method applied to a multi-depot vehicle routing problem with time windows

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Neural computing and Applications 2025年 1-30页

作者： Lim, Ting Yee Ng, Xin Ju Tan, Choo Jun Lim, Chee Peng Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology 77 Lorong Lembah Permai Tiga Penang Tanjong Bungah11200 Malaysia Department of Computing Technologies Swinburne University of Technology John Street HawthornVIC3122 Australia

Evolutionary computation (EC) is a powerful tool for global optimization across various domains, including healthcare logistics. However, the prevalence of local optima in complex problems often hinders the ability of the populations to find optimal solutions, leading to premature convergence. More daunting is that complex optimization problems motivated from real-world challenges continue to pose huge hurdles for EC, with many remaining unresolved. To fill this gap, we propose a new framework named SW-MopGA (Stepwise Monogamous Pairing Genetic Algorithm), which hinges on two innovations. First, a controller for diversity maintenance is introduced into MopGA to generate perturbed opposite solution via opposition-learning when necessary. In doing so, the algorithm is able to maintain population diversity effectively, which alleviates the risk of premature convergence. Second, SW-MopGA proposes a two-step framework: Initially, the population undergoes evolution on a simplified version of the originally complex problem. Solutions derived from the simplified problem serve as building blocks (stepping stones) upon which more intricate blocks are constructed for the final problem—a form of incremental evolution. Numerical analysis on two real-world cases in the logistics healthcare sector demonstrates the effectiveness of SW-MopGA. The problems are formulated as a multi-depot vehicle routing problem with time windows (MDVRPTW). Overall, the combined effect of the two innovations resulted in up to 18% and 8% savings in terms of total travel time and distance, and vehicles used, respectively on an off-peak day scenario;while approximately 17% and 7% savings for the same criteria on a peak-day scenario, compared to the baseline MopGA. Additionally, SW-MopGA improved the best-known solution of three instances in the MDVRPTW benchmark dataset. Finally, algorithmic insights are provided. In general, our initial study shows encouraging outcomes for incremental evolution and its

关键词： Travel time

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Feature selection before propositionalization of multi-source oil drilling data

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Journal of Integrated Design and Process Science 2025年第Pre-press期Pre-press卷 1-31页

作者： Wen, Clement Ting Pek Hui, Patrick Then Hang Lau, Man Fai Centre for Digital Futures Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computing Technologies Swinburne University of Technology Hawthorn Australia

Despite recent improvements in collected drilling data quality and volume, the actual number of wells being used in studies remain low and are often limited to a single source and oil field, producing results that are prone to overfitting and are non-transferable. In our study, we access oil drilling data from 5 of more than 20 oil drilling companies collected from 2005 to 2016 from our industrial partner to create well drilling duration models for well planning. This project could lead to the creation of more generalized models from larger datasets than others in literature. However, the data is difficult to process without expert knowledge, further complicated by properties such as unharmonized, source-locked, semantic heterogeneity, sparse and unlabelled. Conventional automated methods for feature selection, propositionalization, multi-source, or block-wise missing techniques could not be used. In this paper, we describe our method to assist the Knowledge Discovery in Databases (KDD) Selection stage of the abovementioned data - Feature Selection before Propositionalization (FSbP) via Database Attribute Health Feature Reduction (DAHFR) and Report Feature Correlation Matrix (RFCM), collectively known as FvDR. DAHFR and RFCM are filter-type feature selection techniques that could measure relational missingness and keyword correlations respectively despite the complexity of multi-source oil drilling data. FvDR successfully reduced the scope from 700 tables containing 20,000 columns to 22 tables containing fewer than 707 columns while successfully selecting 13 of 16 relevant tables suggested by literature. Despite the loss of information from limitations of subsequent KDD procedures, preliminary models show promising results with over half the test predictions falling within the 20% error margin required for well planning. FvDR proves to be indispensable in KDD as a FSbP framework as it reduces features for examination and streamlines the research process necessary to

关键词： Feature selection before propositionalization big data harmonization knowledge discovery in databases oil drilling

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Deep Learning Approach for Identifying Red Lesions in Retinal Fundus Imagery 6th

Deep Learning Approach for Identifying Red Lesions in Retina...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Divyashrree, R. Dhawan, Samarth Saranya, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9789819774258

One dangerous side effect of diabetes that affects the eyes is called diabetic retinopathy. It happens as a result of alterations in the retina’s blood vessels, which can cause harm and even blindness. The development of red lesions in the retina is one typical sign of diabetic retinopathy. These red lesions may be a sign of microaneurysms or hemorrhages, which are important characteristics to identify and track as diabetic retinopathy progresses. This process becomes even more demanding in the early stages of the disease where symptoms are less pronounced in the images. Leveraging machine-based learning for medical image analysis, especially deep learning algorithms, has demonstrated its capability to enhance the early diagnosis of diabetic retinopathy, providing a more efficient and effective alternative to traditional manual assessments. The proposed work aims to improve the efficiency of early detection of diabetic retinopathy by utilizing Deep Learning (16-layer CNN) to determine a person’s likelihood of having diabetic retinopathy using a 02-Class model that compiles a variety of datasets, specifically IDRiD dataset. The entire dataset contains 18,590 fundus photographs, in which 3662 are allocated for training, 1928 for validation, and 13,000 for testing. All datasets had a similar distribution of output classes, which is a fundamental property of this type of data. Using the IDRiD dataset, the suggested Red Lesion detection system’s specificity and sensitivity were found to be 98% and 89%, respectively, with an accuracy of 95.32%. Using the MESSIDOR dataset, the Diabetic Retinopathy Severity Classification System’s specificity, sensitivity, and accuracy were found to be 93.8, 92.3, and 94%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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Adaptive network combination for single-image reflection removal:a domain generalization perspective

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Frontiers of Computer Science 2025年第1期19卷 171-173页

作者： Ming LIU Jianan PAN Zifei YAN Wangmeng ZUO Lei ZHANG School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China

1 Introduction Recently,multiple synthetic and real-world datasets have been built to facilitate the training of deep single-image reflection removal(SIRR)***,diverse testing sets are also provided with different types of reflections and ***,the non-negligible domain gaps between training and testing sets make it difficult to learn deep models generalizing well to testing *** diversity of reflections and scenes further makes it a mission impossible to learn a single model being effective for all testing sets and real-world *** this paper,we tackle these issues by learning SIRR models from a domain generalization perspective.

关键词： testing generalization while

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Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 459-473页

作者： Ponnuviji, N.P. Nirmala, G. Kokila, M. L. Sworna Priyadharshini, S. Indra Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Thiruvallur India Department of Computer Science and Engineering RMD Engineering College Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nādu Kattankulathur Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GWO) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min–Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing

关键词： Extraction

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