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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Srivastava, Neeraj Kumar Pandey, Prafull Mishra, Manoj Kumar Mishra, Vikas Department of Computer Science and Engineering United Institute of Technology Prayagraj Naini India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj Naini India Department of Computer Applications United Institute of Management Prayagraj Naini India

ISBN: (纸本)9789819930098

The use and importance of educational data mining (EDM) are growing very fast as the entire world is going digital. The predictions of EDM [1, 2] are playing very important role not only in improving the academic performance of individual students but also are very useful for institutions, policy makers, administrators to plan and design new policies that will certainly help them to achieve high academic performance of their students. On this basis, this paper approaches students’ performance in secondary school of two Portuguese schools. The informational elements comprise demographic, societal, and educational [3] features. Although there are many ML [4, 5] algorithms, yet in this paper, five ML classifiers such as random forest, Naïve Bayes, logistic regression, decision tree, and support vector machine have been used to predict the students’ performance that on the basis of given features, whether a student can be passed in secondary education or not. On the dataset, the Naïve Bayes classifier gives the best performance than other classifiers, when the dataset is partitioned into 80–20 ratio as training and test datasets. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Heart disease Classification Utilising Machine Learning Techniques 3

Heart disease Classification Utilising Machine Learning Tech...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Dhondiyal, Shiv Ashish Verma, Himanshu Kumar, Rohit Sawan, Vikash Uniyal, Ishita Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India Haridwar University Roorkee Dept. of Computer Science and Engineering India GLA University Department of Computer Engineering & Applications Mathura India Era Hill University Department of Electrical Engineering Graphic India

ISBN: (纸本)9798350354171

Heart diseases comprise a extensive Various heart conditions and are commonly referred to as cardio vascular diseases. These encompass issues with the rhythm of the heart, blood vessel diseases, and congenital cardiac defects. It has been the world's biggest cause of death for the last few decades. Finding a reliable and accurate way to automate the process of early disease diagnosis and efficient disease management is urgently needed. Data science facilitates the medical sciences in analysing vast quantities of data. Researchers utilize various Data Mining and Machine Learning methods to evaluate extensive datasets in order to accurately forecast heart illness. This study examines the supervised learning models of Random Forest, K-NN, and SVM to provide a comparative analysis of their efficiency. It is discovered that, when compared to other algorithms, SVM offers the highest accuracy at 0.994. © 2024 IEEE.

关键词： Random forests

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Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and Machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 IEEE International Conference on Intelligent Systems and Advanced applications, ICISAA 2024

作者： Vasumathy, M. Devakanth, J. Jude Moses Anto Thirugnanam, Mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. Major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 IEEE.

关键词： Drones

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective Cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing Cloud Services: Streamlining Performance Metrics and Cloudlet Scheduling 2

Enhancing Cloud Services: Streamlining Performance Metrics a...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Gritto, D. Vasughi, T.S. Department of Computer Science SRM Arts and Science College Tamilnadu Chengalpet603 203 India Department of Computer Applications and Technology SRM Arts and Science College Tamilnadu Chengalpet603 203 India

ISBN: (纸本)9798350340235

Enhancing cloud service is the primary goal of cloud stakeholders. It is a comprehensive goal that can be achieved through optimizing performance metrics, effective resource utilization, and prioritizing user satisfaction. These performance metrics, also called Quality of Service (QoS) metrics, are mentioned in the Service Level Agreement (SLA), a contractual document. Optimizing metrics, resource utilization, and user experience requires continuous monitoring systems and cloud technologies such as virtualization, scheduling, migration, consolidation, load balancing, etc. Scheduling cloudlets, virtual machines, and load balancing are crucial for achieving the SLA enumerated QoS and other key demands. In order to monitor the performance and evaluate the effectiveness of any cloud service knowledge on key performance metrics, scheduling and load balancing become imperative. This paper is orchestrated to identify essential performance metrics and explore how scheduling and load balancing algorithms enhance cloud performance. Additionally, it seeks to conduct a comparative evaluation of FCFS, SJF, Min-Min, Max-Min, RASA, Suffrage, and TASA cloudlet scheduling algorithms using CloudSim. This evaluation focuses on key scheduling metrics, including average waiting time, makespan, virtual machine utilization ratio, and load balancing. © 2023 IEEE.

关键词： Load Balancing Metrics QoS Scheduling SLA

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Telecom Churn Classification using Scatter Search and Random Forest Classifier

Telecom Churn Classification using Scatter Search and Random...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Varun, A.S. Babu, Tina Nair, Rekha R Kishore, S. Dayananda Sagar Univerity School of Engineering Department of Computer Science & Engineering Bengaluru India Alliance Univerity Department of Computer Science and Engineering Bengaluru India Nitte Institute of Professional Education Nitte University Department of Computer Applications Karnataka India

ISBN: (纸本)9798350313413

The Telecom Churn Dataset is a valuable resource that contains customer-related information such as demographics, usage patterns, billing data, and customer service interactions. The proposed model is used to explore various factors that might impact churn behavior and apply machine learning techniques to create predictive models for proactive retention efforts. This study have employed different approaches and methods, including random forest, logistic regression, and decision trees, as well as feature selection techniques like Correlation-based Feature Selection (CFS), scatter search etc. The proposed methodology involves preprocessing the data using techniques like SMOTE to handle class imbalance, selecting relevant features through Scatter search, and applying classification algorithms like the Random Forest (RF) classifier. The proposed model using Scatter search and Random Forest classifier has best formed with an accuracy of 91.9%, precision and recall of 0.919. Thus the model is well suited for telephone churn classification with the optimized feature set selection and classification. © 2023 IEEE.

关键词： Classification feature reduction random forest SMOTE Telecom churn

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Machine Learning for Translating Pseudocode to Python: A Comprehensive Review 7

Machine Learning for Translating Pseudocode to Python: A Com...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Tiwari, Satya Prakash Prasad, Shivam Thushara, M.G. Amrita Vishwa Vidyapeetham Amritapuri Amrita School of Computing Department of Computer Science and Applications Kollam India

ISBN: (纸本)9798350397253

Translating pseudocode to a programming language can be difficult and time-consuming, especially for individuals unfamiliar with the languages involved. However, recent advances in Natural language processing (NLP) have made it possible to create a model that can automatically convert pseudocode written in natural language to code in a specific programming language. This research study proposes a solution to this problem by using an NLP transformer model to translate pseudocode into Python code automatically. The model will be trained on pseudocode examples and corresponding Python code translations. This study leverages a large dataset of pseudocode examples and their corresponding Python code translations to achieve this. By training the model on this dataset, it will be able to learn the patterns and structures present in the pseudocode and match them with their corresponding Python syntax. This will allow the model to translate pseudocode accurately into Python code, even for complex algorithms and structures. © 2023 IEEE.

关键词： Large dataset

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A Hybrid Approach for Automated GUI Testing Using Quasi-Oppositional Genetic Sparrow Search Algorithm

A Hybrid Approach for Automated GUI Testing Using Quasi-Oppo...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Jayalakshmi, N. Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

Testing of GUI is crucial for assessing software reliability, usability, and functionality;however, classical approaches are not sustainable in contemporary applications. It proposes the Quasi-Oppositional Genetic Sparrow Search Algorithm (OOGSSA) to auto-generate efficient GUI test cases. It combines genetic algorithms, sparrow search dynamics, and quasi-oppositional learning to optimize test suites, minimize redundant test cases, and improve the testing fault. The result obtained for the model OOGSSA is 95.8 % in test coverage, 85.3 % in redundancy, and 92.6 % in fault detection. Furthermore, test generation time was minimized to forty minutes and the overall testing time to seventy-five minutes, which is more efficient than conventional genetic or model-based testing. The outcomes presented above show that OOGSSA is highly efficient and effective in solving the issues of the current GUI testing and improving the overall quality and reliability of software. More enhancements to the algorithm will be done in future work as well as leveraging the algorithm to cover a wider GUI space including mobile and web. © 2024 IEEE.

关键词： Software reliability

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Transforming Agricultural Supply Chains: The Power of Blockchain Technology 3

Transforming Agricultural Supply Chains: The Power of Blockc...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kumar, Gotte Ranjith Lovely Professional University School of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Sr University School of Cs & Ai Department of Cse Telangana Warangal506371 India

ISBN: (纸本)9798331515911

Implementing blockchain technology in agricultural supply chains has the potential to revolutionize and increase transparency, efficiency and trust between all parties. A variation of the white paper discussed how blockchain can be a transformative tech in addressing major challenges especially on traceability, fraud prevention and sustainability on agriculture. Blockchain (for those hearing about this for the first time) can provide a system where every step of even complex supply chains is perfectly recorded, enabling transactions to take place in a secure way and offering transparency, immutability, accountability. Case studies and real-world examples showcased throughout the paper depict how blockchain could enhance operational efficiencies, boost food safety, authenticate products, and cut waste across agriculture. While very promising, the implementation of blockchain technology in agriculture is not without its own issues;this includes obligatory industry standards as well as highly costly technical implementations. The paper also highlights the potential for blockchain to contribute to sustainable agriculture by allowing better tracking of environmental footprints and ethical sourcing. In summary, we propose that the flexibility of blockchain technology renders it an ideal approach to revolutionizing supply chains in the agriculture sector, as we continue down a trajectory towards increased levels of visibility and productivity with improved environmental outcomes. This paper contributes towards a more comprehensive understanding of the role of blockchain in scenario-based selection as it addresses both opportunities and challenges due to adoption of block chain which gives technology and agriculture a new future(directions). © 2025 IEEE.

关键词： Sustainable agriculture

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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