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arXiv 2025年

作者： Das, Partha Pratim Department of Computer Science Center for Data Science and Analytics Ashoka University Sonepat India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. A study by the Government and FICCI in 2014 noted that India cannot meet its educational needs just by capacity building in brick and mortar institutions. It was decided that ongoing MOOCs projects under the umbrella of NMEICT will be further strengthened over its second (2017-21) and third (2021-26) phases. NMEICT now steers NPTEL/SWAYAM (India's MOOCs) and several digital learning projects including Virtual Labs, e-Yantra, Spoken Tutorial, FOSSEE, and National Digital Library on India-the largest digital education library in the world. Further, India embraced its new National Education Policy in 2020 to strongly foster online education. In this chapter, we take a deep look into the evolution of MOOCs in India, its innovations, its current status and impact, and the roadmap for the next decade to address its challenges and grow. AI-powered MOOCs is an emerging opportunity for India to lead MOOCs worldwide. © 2025, CC BY-NC-ND.

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Leveraging LLM in Genetic Programming Hyper-heuristics for Dynamic Microservice Deployment 37th

Leveraging LLM in Genetic Programming Hyper-heuristics for...

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37th Australasian Joint Conference on Artificial Intelligence, AJCAI 2024

作者： Fang, Zhengxin Ma, Hui Chen, Gang Hartmann, Sven Wang, Chen Centre for Data Science and Artificial Intelligence and School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand Department of Informatics Clausthal University of Technology Clausthal-Zellerfeld Germany National Institute of Water and Atmospheric Research Wellington New Zealand

ISBN: (纸本)9789819603473

Microservice deployment in cloud computing is a challenging combinatorial optimization problem due to the complex dependencies among microservices and the intricate trade-offs among different QoS requirements, e.g., minimizing Energy Consumption (EC) vs. minimizing Communication Overhead (CO). Recently, some hyper-heuristics methods, particularly Genetic Programming Hyper-Heuristics (GPHH), have been proposed to automatically generate heuristics for solving dynamic microservice deployment problems. Meanwhile, Large Language Models (LLMs) are becoming popular for solving various domain-specific problems thanks to their strong ability to learn problem-related knowledge. However, hybridizing GPHH with LLM by combining their abilities in solving complex optimization problems remains unexplored. In this paper, we propose an LLM-enhanced Genetic Programming Hyper-Heuristic (LLM-GPHH) algorithm to evolve heuristics for the dynamic deployment of applications composed of microservices, to jointly optimize EC and CO. Our experiments on real-world datasets demonstrate the effectiveness of the newly proposed LLM-GPHH. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic programming

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Plant Disease Detection with Artificial Intelligence Algorithms for Smart Agriculture

Plant Disease Detection with Artificial Intelligence Algorit...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： R. Sudeepthi V. Akshitha K. Navya K. Shalini R. Karthikeyan Hariharan Shanmugasundaram Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Overseeing plant leaf diseases is vital for sustaining crop health, preventing extensive agricultural losses, safeguarding farmers livelihoods and ensuring a stable food supply. The health of the plant leaf is severely harmed by diseases such as early and late blight. Detecting leaf diseases is a time-consuming operation, and many farmers lack understanding about it, resulting in crop failure. Thus, early disease detection is crucial since it affects the quality and quantity of the potatoes. A type of artificial intelligence called deep learning has many uses in several domains and enables accurate and efficient data processing and decision-making. In this study, Leaf illnesses like early blight and late blight are detected and predicted using DL architectures like MobileNetV2, ResNet50, and EfficientNetB0. A comprehensive dataset is employed, containing images of healthy and infected leaves, which aids in model training and evaluation. The results show that MobileNetV2 achieves the highest accuracy of 91% in identifying leaf diseases.

关键词： Deep learning Accuracy Plants (biology) Transfer learning Crops Livelihood Convolutional neural networks Artificial intelligence Diseases Residual neural networks

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Enhancing Cloud Security Implementing AI-Based Intrusion Detection Systems 1

Enhancing Cloud Security Implementing AI-Based Intrusion Det...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Santharam, Vinoth Thoguluva Yallamelli, Akhil Raj Gaius Altemimi, Mohammed Alaa H. Arunachalam, Mahesh Prabu Rudrank, Shukla Ghule, Gauri Data Analytics Organization-Comcast Chennai India Amazon Web Services Inc Seattle United States Al-Khwarizmi College of Engineering The University of Baghdad Department of Information and Communication Engineering Baghdad Iraq Department of Information Technology and Computer Science AustinTX United States Kalasalingam Academy of Research and Education Deemed to be University Tamilnadu India Department of E and TC Pune India

ISBN: (纸本)9798331518578

The increasing complexity of assaults necessitates the use of innovative intrusion detection systems (IDS) to safeguard critical assets and data. There is a higher risk of cyberattacks like data breaches and unauthorised access since cloud services have been used more frequently. The project's goal is to find out how Artificial Intelligence (AI) could enhance the IDS's ability to identify and classify network traffic and identify anomalous activities. Online dangers could be identified with IDS. An intrusion detection system, or IDS, is required to keep networks secure. We must create efficient IDS for the cloud platform as well, since it is constantly growing and permeating more aspects of our daily life. However, using standard intrusion detection systems in the cloud may provide challenges. The pre-established IDS design may overburden a cloud segment due to the additional detection overhead. Within the framework of an adaptively designed networked system. We demonstrate how to fully use available resources without placing undue load on any one cloud server using an intrusion detection system (IDS) based on neural networks. To even more successfully detect new threats, the suggested IDS make use of neural network machine learning (ML). © 2025 IEEE.

关键词： Network intrusion

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Advancing Urban Analysis through Joint Rooftop Segmentation and Building Height Estimation

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Procedia computer Science 2025年 260卷 947-956页

作者： Ayan Gupta Mayank Dixit Vipul Kumar Mishra Georg-August university of Göttinge Germany Department of Computer Science & Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Artificial Intelligence & Data Science Gati Shakti Viswavidyalaya Vadodara India

Accurate segmentation of building rooftops holds significant importance within the realm of urban analysis. The extraction of building structures from high-resolution imagery represents a pivotal task that facilitates insights into urban growth, infrastructure planning, and disaster management. However, the sole segmentation of building rooftops falls short of complete urban planning, as a crucial attribute, building heights, remains absent. For this vital task of height estimation, digital surface models (DSMs) are employed which provide valuable height information about the Earth’s surface. Furthermore, the integration of DSMs with various types of remotely sensed data has shown success across a broad spectrum of applications. Within the context of deriving more profound insights for enhanced urban analysis, this research paper presents a comprehensive approach which utilizes Squeeze and Excite technique along with residual blocks to jointly perform binary rooftop segmentation and predicting the height of building structures. The work leveraged the rich dataset made available by the 2023 IEEE GRSS data Fusion Challenge. The building annotations provided in the dataset are used to form the building binary masks and normalized DSMs. Another aspect of this work involves utilizing RGB satellite imagery of the used dataset to create binary segmentation masks of building rooftops and normalized DSMs. In terms of statistical results, our approach which uses the adaptive recalibration of channel-wise features when compared with other state-of-art algorithms yields an impressive F1 Score of 0.93 for rooftop segmentation and a Zero Normalized Cross-Correlation (ZNCC) score of 0.88 for height estimation on the test dataset. This approach offers a flexible and accurate solution to address the multiple, interconnected joint tasks of building segmentation and its height estimation simultaneously, thereby reducing the training and testing times proportionally.

关键词： Rooftop segmentation Remote Sensing Deep Learning Height Estimation Digital Surface Model (DSM) Image Processing

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Optimizing Energy Efficiency via Small Cell-Controlled Power Management for Seamless data Connectivity 27th

Optimizing Energy Efficiency via Small Cell-Controlled Powe...

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27th International Conference on Distributed computer and Communication Networks, DCCN 2024

作者： Shabbir, Amna Ahmad, Sadique Azhar, Madeeha Rizvi, Safdar Ali Kushchazli, Anna Ateya, Abdelhamied Ashraf Department of Electronic Engineering NED UET Karachi Pakistan EIAS Data Science and BlockChain Lab CCIS Prince Sultan University Riyadh Saudi Arabia Department of Telecommunication Engineering NED UET Karachi Pakistan Computer Science Department Bahria University Karachi Campus Karachi Pakistan RUDN University 6 Miklukho-Maklaya Street Moscow Russia Zagazig University Zagazig Egypt

ISBN: (纸本)9783031808524

The growing importance of energy-efficient networks capable of handling high data rates poses a significant concern for network operators. With the evolution of new mobile data network standards, there has been a surge in the number of subscribers and an increased demand for high-speed data traffic, enabling seamless streaming of social media content within seconds. Network operators are tasked with meeting consumer satisfaction, and to achieve high data rates and good signal quality, they have adopted the deployment of small cells. In this research, energy efficiency is enhanced through an investigation of the small cell-controlled scheme, which is then extended to incorporate a power control strategy based on throughput measurements. Additionally, energy consumption is reduced by adjusting the transmit power based on traffic patterns. Specifically, during periods of normal traffic, the power utilization is set at 40% of the total power, while in times of increased traffic intensity, 60% of the total power is allocated. This traffic intensity-based power distribution scheme results in a 13–15% increase in energy efficiency compared to the small cell-controlled sleep mode. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： energy efficiency power control power distribution small cell

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DDoS Classification Through Web Application: Leveraging Algorithms for Cybersecurity

DDoS Classification Through Web Application: Leveraging Algo...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Bezawada Sasaank Mukesh Chinta Basireddy Duggireddy Redipalli Lalitha Karthik Paturi Sai Sri Vatsa Department of Artificial Intelligence & Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Andhra Pradesh Department of Computer Sciience and Engineering Velagapudi Ramakrishna Siddhartha

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This study and project proposes a method for classifying Distributed Denial of Service (DDoS) attacks using the Random Forest Algorithm, with a focus on determining measurement metrics for distinguishing between DDoS attacks and benign network activity. DDoS attacks represent a significant threat to network availability and security, necessitating robust classification mechanisms. The Random Forest Algorithm is employed due to its effectiveness in handling large pattern datasets and its ability to detect complex attack patterns. By analyzing various measurement metrics such as packet rate, packet size distribution, and traffic volume, features indicative of DDoS attacks are identified. These features are then used to train the Random Forest model to classify incoming network traffic as either DDoS or Benign. Evaluation of the classification performance is conducted using metrics such as accuracy, precision, recall, and F1 score, providing insights into the effectiveness of the proposed approach. Experimental results demonstrate the ability of the Random Forest Algorithm to accurately classify DDoS attacks while minimizing false positives, thereby enhancing network security and mitigating potential disruptions to web services.

关键词： Support vector machines Accuracy Web services Volume measurement Telecommunication traffic Denial-of-service attack Prediction algorithms Classification algorithms computer crime Random forests

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Identifying Sources of Soil Pollution in Industrial Areas Using IoT data and Neural Networks

Identifying Sources of Soil Pollution in Industrial Areas Us...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kovarasan, Rajesh Kambattan Shibu, N.V. Sairam, A. Manivannan, K.K. Ishwarya, M.V. GaneshBabu, T.R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Mechatronics Engineering KCG College of Technology Tamil Nadu Chennai India Artificial Intelligence and Data science Department Agni College of Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9798350374933

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IoT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns. © 2024 IEEE.

关键词： Environmental monitoring

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Clustering Single-Cell RNA-seq data: Impact of data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： data accuracy

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Hybrid Approach of TabNet and Transformer-XGBoost for Predicting Traffic Flow in Smart Cities 1

Hybrid Approach of TabNet and Transformer-XGBoost for Predic...

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1st IEEE International Conference on Smart and Sustainable Developments in Electrical engineering, SSDEE 2025

作者： Hole, Shreyas Rajendra Kolluru, Vinothkumar Salotagi, Shreekant Challagundla, Yagnesh Mungara, Sudeep Naik, Mude Nagarjuna Bangalore India Stevens Institute of Technology Department of Data Science HobokenNJ07030 United States Dayananda Sagar University Computer Science & Engineering Bangalore India Herbert Wertheim College of Engineering University of Florida Department of Computer Science GainesvilleFL32611 United States Stevens Institute of Technology Masters in Information Systems HobokenNJ07030 United States

ISBN: (纸本)9798331542108

A smart city's transportation system can be en-hanced if there is an accurate prediction of the traffic flow which develops in an area as time goes on. This article investigates the relative accuracy of predicting traffic flow at target intersections in a city, taking advantage of two models: Tab Net and a hybrid Transformer-XGBoost. Several metrics including Mean Squared Error (MSE) and Coefficient of determination (R2 score) among others were employed to evaluate the models employed. A sequential deep learning model known as Tab Net triumphing in the competition with MSE, RMSE, and MAE being 34.48, 5.87, and 3.73 respectively, and R2 score as high as 0.917, although performing well while enhancing identification of features such as year and junction. On the other hand, while reporting similar R2 score as low as 0.184, the Transformer-XGBoost hybrid model had structuring limitations where complex interdependencies and time variations were involved recording MSE, RMSE and MAE as 338.52, 18.40 and 13.24 respectively. The graphs of residuals and QQ plots served to uncover that the hybrid model failed making a right prediction during high traffic volume days, as general tabnet results shown in the final graphs are more stable. Following investigation of the models in this study, the researchers suggest implementing TabNet as a better option for traffic management explaining its high accuracy and providing valuable guidance for cities. This would result in improvement of the traffic flow prediction, which is highly needed for effective urban mobility solutions. © 2025 IEEE.

关键词： Smart city

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