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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Ahamed, B. Shamreen Poornima, D. Sheela, A.C. Santha Nivetha, S. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Allocating sources correctly within the ever-changing world of cloud computing is vital for maintaining uninterrupted guide of apps and offerings at the same time as preserving charges down. Machine mastering's flexibility to accommodate unique duties and person conduct makes it an appealing option for assembly those desires. As a end result of factors including variable workloads, special application desires resource allocation in the cloud area provides a number of difficulties. Allocation strategies based on static parameters generally fail to fulfill these demanding situations. By integrating past facts, future predictions, and immediately feedback, MLT provide a promising opportunity for developing a flexible and powerful technique of allocating resources. This paper introduces a novel approach to cloud useful resource allocation referred to as Dynamic Resource Allocation with Reinforcement Predictive Learning (DRA-RPL). DRA-RPL combines reinforcement studying with predictive analytics to provide a flexible allocation mechanism that could respond to converting requirements in actual time. This technique seeks to find the candy spot between performance, efficiency, and cost to assure swift and powerful deployment of assets. DRA-RPL uses a cloud-based totally reinforcement mastering agent. The workloads, useful resource availability, and alertness performance are in reality some of the factors that this agent is continuously tracking. The technique uses predictive analytics to foresee useful resource demands primarily based on previous statistics and patterns. This predictive thing enables the reinforcement mastering agent count on future requirements. The simulation effects show the way the approach handles versions in surroundings and workload, imparting sturdy evidence of its effectiveness. With the ability to reinforce resource utilization, fee-effectiveness, and client delight across cloud-based totally offerings, DRA-RPL is a possible method that would help

关键词： Resource allocation

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Sustainable Development of Employability and Skill Development in Outcome-based Education: A Machine Learning Approach 2

Sustainable Development of Employability and Skill Developme...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Bora, Mousoomi Baruah, Rupam Assam Science and Technology University Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9781665491990

The contribution of technological enhancement has played a vital role in strengthening the country's economy. The Higher Education lnstitutions (HEI) are providing placement assistance to the students by employing various measures as a part of their curriculum. A few of the attempts that HEIs have adopted to enhance soft skills as well as hard skills of the students are, course structure modification, student-mentoring, industry-academia collaboration etc. In spite of all the noble efforts, many unemployed engineers are witnessed in the society. Therefore, this research aims to develop an effective and accurate framework that would be highly beneficial for both students as well as teachers in HEIs. A Semi-Supervised-Sequence-Prediction (SSSP) machine learning framework has been designed to predict students' academic achievement towards the completion of a four-year degree course in an outcome-based education system. A time series analysis of students' data has been conducted to analyze the progression of final cumulative GPA and employability prospect in sequential manner. The results show the effectiveness of the proposed framework in predicting employability of students along with the areas where students need improvement in order to be employable. This research provides an inclusive roadmap, empowering the use of machine learning for employability and skill development in outcome-based education. © 2023 IEEE.

关键词： Students

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Text-to-Sketch Synthesis via Adversarial Network

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computers, Materials & Continua 2023年第7期76卷 915-938页

作者： Jason Elroy Martis Sannidhan Manjaya Shetty Manas Ranjan Pradhan Usha Desai Biswaranjan Acharya Department of Information Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia Department of Computer Science and Engineering NMAM Institute of Technology(Nitte Deemed to be University)NitteIndia School of Computing Skyline University CollegeUniversity CitySharjahUAE Department of Electronics and Communication Engineering SR UniversityWarangalTelanganaIndia Department of Computer Engineering–AI&BDA Marwadi UniversityRajkotGujaratIndia

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security ***,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch *** the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch *** this study,we propose an adversarial network that generates human facial sketches using such cues provided by an ***,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated *** evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance *** proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

关键词： Generative adversarial networks inverse gamma correction sketch attributes text-to-sketch synthesis deep learning techniques

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Vehicle Motion Prediction Based on Selective Perception and Weak Lane Guidance 29

Vehicle Motion Prediction Based on Selective Perception and ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Xiaoliang Zheng, Shiqi Zou, Junkang Liu, Yuzhen Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350330717

Trajectory prediction is crucial for ensuring the safety and reliability of autonomous driving systems. Accurately predicting the future trajectory of a vehicle can aid drivers or automated driving systems in developing reasonable movement plans and avoiding potential collisions. In real-world scenarios, drivers selectively observe areas that influence their driving decisions based on real-time traffic scenarios, adapting to changing traffic situations to ensure safety. To simulate this behavior, we propose a selective perception module that utilizes historical trajectories and lane centerlines to extract vital information from the changing traffic scene. Furthermore, vehicles tend to follow lane guidance to some extent, we use a normalized flow decoder and small-scale guided loss to induce the distribution of predicted trajectories, slightly increasing the probability of lane centerlines in the predictions as a weak lane guidance. We conduct ablation studies using the public dataset nuScenes and comparative experiments with state-of-the-art methods, showcasing the predictive diversity of our approach while maintaining high accuracy. © 2023 IEEE.

关键词： autonomous driving motion prediction

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Unveiling Hidden Patterns: Exploration of Data Dependencies in Disease Prediction

Unveiling Hidden Patterns: Exploration of Data Dependencies ...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Durga Devi, P. Akila, K. Srm Institute of Science and Technology Department of Computer Science and Engineering Vadapalani India

ISBN: (纸本)9798350394580

In the current scenario, the possibility of getting affected by disease is high so it is important to predict in advance. Due to technological advancement, it is for early prediction. Hence, this paper is focused on analysis of various DL algorithms for prediction system. This work provides a comprehensive report for the disease prediction system with the analysis of test input parameters for different disease. Further, different algorithms useful for the prediction framework has been discussed. Studying the interdependence of these risk factors in patients' medical records and comprehending their respective contributions to stroke prediction is critical. Deep learning techniques are crucial in overcoming these issues since they enable automatic disease identification in healthcare facilities. The prediction framework is analyzed using data from the Electronic Health Record dataset. Parametric statistics are used to evaluate the results. © 2023 IEEE.

关键词： Electroencephalogram(EEG) Electronic Health Record(EHR) Gated Recurrent Unit(GRU) Long Short Term Memory(LSTM) Recurrent Neural Network(RNN)

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Detection of Acute Lymphoblastic Leukemia Using a Novel Bone Marrow Image Segmentation

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Tsinghua science and technology 2025年第2期30卷 610-623页

作者： M.Anline Rejula Ben M Jebin Ravi Selvakumar S.Amutha Eberlein George Department of Computer Applications-PG Scott Christian College(Autonomous)Nagercoil 629003India Department of Computer Science Malankara Catholic CollegeMariagiri 629153India School of Computer Science and Engineering Vellore Institute of TechnologyChennai 600127India Department of Pediatrics Government Medical College and HospitalGovernment of Tamil NaduNamakkal 637001India

In our study,we present a novel method for automating the segmentation and classification of bone marrow images to distinguish between normal and Acute Lymphoblastic Leukaemia(ALL).Built upon existing segmentation techniques,our approach enhances the dual threshold segmentation process,optimizing the isolation of nucleus and cytoplasm *** is achieved by adapting threshold values based on image characteristics,resulting in superior segmentation outcomes compared to previous *** address challenges,such as noise and incomplete white blood cells,we employ mathematical morphology and median filtering *** methods effectively denoise the images and remove incomplete cells,leading to cleaner and more precise ***,we propose a unique feature extraction method using a hybrid discrete wavelet transform,capturing both spatial and frequency *** allows for the extraction of highly discriminative features from segmented images,enhancing the reliability of *** classification purposes,we utilize an improved Adaptive Neuro-Fuzzy Inference System(ANFIS)that leverages the extracted *** enhanced classification algorithm surpasses traditional methods,ensuring accurate identification of acute lymphoblastic *** innovation lies in the comprehensive integration of segmentation techniques,advanced denoising methods,novel feature extraction,and improved *** extensive evaluation on bone marrow samples from the Acute Lymphoblastic Leukemia Image DataBase(ALL-IDB)for Image Processing database using MATLAB 10.0,our method demonstrates outstanding classification *** segmentation accuracy for various cell types,including Band cells(96%),Metamyelocyte(99%),Myeloblast(96%),***(97%),***(97%),and Neutrophil cells(98%),further underscores the potential of our approach as a high-quality tool for ALL diagnosis.

关键词： Adaptive Neuro-Fuzzy Inference System(ANFIS) blood cells segmentation leukemia classification

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Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer's 3

Deep 3D-CNN using Resonance Imaging for Diagnosing Alzheimer...

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3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023

作者： Ayub, Nadish Shah, Syed Zubair Ahmad Assad, Assif Din, Nusrat Mohi Ud Islamic University of Science and Technology Department of Computer Science and Engineering Awantipora India

ISBN: (纸本)9798350320749

Alzheimer's Disease (AD), the most prevalent type of dementia, is an incurable neurological disorder that leads to progressive mental decline. The majority of an AD diagnosis in practise is based on the patient's clinical history and neu- ropsychological information, such as magnetic resource imaging, despite the fact that an exact diagnosis of AD is challenging (MRI). Deep learning's use in recognising AD is the subject of growing research in recent few years. It enables for automatic AD identification using 3D brain MRI images. For AD recognition, the network makes use of a three-dimensional convolutional neural network (3DCNN). It is distinctive in that AD recognition accurately reflects the three-dimensional structure of the brain as a whole. Due to the fact that MRIs are 3D objects and utilise 3D Convolutional Neural Networks (CNNs) since using 3D CNNs the spatial integrity is maintained as compared to other methods that only employ a section of the MRI, such as ROI-based CNNs which works on some specific ROI, 2D slices that uses 2D slices, and 3D Patch-level that use 3D pathes. Four successive processing layers, two fully connected layers, and a classification layer is employed to design the CNN in this study. Each of the three groups in the structure is composed of three layers: a convolutional layer, a pooling layer, and a normalisation layer. The AD Neuroimaging Initiative's MRI data is used to train and evaluate the algorithm. The data used in this work is comprised of 1656 MRI scans of 382 patients. According to the results of the trial, the suggested algorithm had a 99% training accuracy of AD recognition with a validation accuracy of 73%. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Spider Monkey Based K-Means Dynamic Collaborative Filtering for Movie Recommendation Systems 1

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14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022

作者： Devi Priya, R. Reddy, Boggala Thulasi Hariharan, P. Department of Computer Science and Engineering Centre for IoT and Artificial Intelligence KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9783031275241

ISBN: (纸本)9783031275234

Building Automation Recommender Systems (BARSs) can keep constructing proprietors’ cash by means of lowering power intake whilst concurrently retaining occupant comfort. There are algorithms that optimize this exchange off, including detecting which home equipment have become the one without requiring costly popularity detectors to be connected to every appliance. However, efficient methods have to be identified which are best suited for specific constructing. Recommender structures were used extraordinarily, academically and commercially. The guidelines are generated by using methods which are capable of providing applicable thrilling gadgets to customers. Several techniques were proposed for offering customers with guidelines for the use of their score records. Most of those techniques are afflicted by new person problem (cold-begin) which is the preliminary loss of gadgets reviews. In this paper, demographic statistics are used to offer guidelines rather than the use of score records to keep away from cold-begin problem. Spider Monkey based K-Means Dynamic Collaborative Filtering is proposed for movie recommendations and the results are satisfactory. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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The Use of Recurrent Neural Networks in the Optimization of computer science Algorithms 1

The Use of Recurrent Neural Networks in the Optimization of ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Franklin, Ramya G Ronald Doni, A. Poornima, D. Sabasti Prabu, S. Igni Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

A new era of computational efficacy and problem-solving abilities has begun with the combination of Recurrent Neural Networks (RNNs) and computer science methods. It is crucial in modern computing to combine RNNs with methods from the field of computer science. RNNs bring a dynamic learning paradigm, making algorithms more efficient and flexible by allowing them to process sequential input and complicated dependencies. Their usefulness goes beyond one specific area of study, and into others such as optimization issues, recommendation systems, image processing, and natural language processing. RNNs serve a vital role in addressing the severe challenges that algorithms confront, including processing sequential input, identifying temporal connections, and optimizing in high-dimensional environments. By providing dynamic and adaptive modeling, RNNs aid algorithms in navigating these obstacles. Recurrent Neural Network-Based Optimization Algorithm (RNN-OA) is an innovative novel approach presented in this research. RNN-OA is an approach to algorithmic optimization that makes use of RNNs' dynamic learning capability. Mechanisms for selective data processing with attention, regularization methods for model stability, and interpretability improvements for openness are all a part of it. Quickening the process of optimization in many different types of problems is transfer learning and fine-tuning approaches. There are a wide variety of computer science fields where RNN-OA can be useful. Time-series analysis, language modeling, picture recognition, and recommendation systems all benefit greatly from its ability to handle sequential data. The efficacy, scalability, and robustness of the method are thoroughly evaluated by simulating various problem scenarios and data streams. The results of these simulations provide illumination for the benefits and drawbacks of RNN-OA, paving the way for its development toward actual application in algorithmic optimization. © 2023 IEEE.

关键词： Recurrent neural networks

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Optimized Task Scheduling in Diverse Distributed Systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Muthusamy, Ramesh Kumar Kadu, Swati Ayyasamy, Karthikeyan Pichuka Veera Venkata Satya, Srinivas Arumugam, Ramachandran Ammasai, Viswanathan Sengan, Sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed Systems (HDS), efficient task scheduling (TS) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task Scheduling (TS). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The TS dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform TS approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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