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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Kumar, Deepika Srivastava, Varun Gupta, Shilpa Jamil, Akhtar Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Science and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Computer Science and Engineering JIMS Engineering Management and Technical Campus Uttar Pradesh Greater Noida India Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan

ISBN: (纸本)9783031823763

Brain tumour besides being lethal can also affect other human organs on a long-term basis if not detected at an early stage. Based on the region of its presence and speed of growth, it can be classified as Glioma, Meningioma. or Pituitary. In the given work, a Stacked Neural Net approach for the classification of brain tumour is presented. The images are first pre-processed using Histogram equalization and One Hot Encoding. Thereby, the improved images are fed into 3 popular pre-trained deep networks viz. Res-Net-50, VGG-16, and Xception as level-0 models in parallel. The features extracted are then fed into a neural network as level 1 learner for the final classification. A publicly available Figshare dataset has been used for testing the performance. The suggested approach has been thoroughly evaluated against its base models using accuracy, precision, and recall metrics and also the performance has been analysed using accuracy and loss curves along with epochs. The suggested model achieved a validation and training accuracy of 97.3% and 98.12% respectively. It has been observed that the suggested model referred here as Stacked-Neural-Net outperforms when compared to the conventional deep learning methods and state-of-the-art methods. An improvement of 3.35%, 1.3% and 1.7% is achieved over Inception ResNetV1, a basic CNN and Alex-Net CNN models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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FRCD: Future Ready Career-Duck a Machine Learning-Driven Career Guidance for Students

FRCD: Future Ready Career-Duck a Machine Learning-Driven Car...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Paritosh, Priyanshu Yadav, Vedant Gupta, Dev Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Mechatronics and Automation Engineering Bhagalpur India Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India

ISBN: (纸本)9798331540821

In today's society, finding a suitable career is crucial. High school students frequently need to fully understand the notion of labor due to a lack of experience and practical knowledge in various disciplines. This renders students particularly vulnerable to external influences while deciding on a career path. As a result, students must carefully consider high-paying careers to ensure that they align with their interests and skills. Otherwise, students may end up in jobs they dislike or are not suited to. In response to this issue, we intend to develop a machine-learning framework capable of predicting career pathways. Students will receive career recommendation from our model based on their performance and potential. Students can confidently follow their chosen fields if they do well academically;if not, they can choose to change to other fields that better suit them. There are challenges in determining the metrics and improving prediction accuracy to quantify students' career paths and performance in teaching-learning. Best on the test results, the proposed model has a higher prediction accuracy. Also, this method is both reliable and portable, and it may be used to predict career paths in real time. © 2024 IEEE.

关键词： Students

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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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An optimal feature selection-based deep learning approach for wheat disease identification

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Multimedia Tools and Applications 2025年第14期84卷 12855-12876页

作者： Raja, S. M. Naveen Nargunam, A. Shajin Department of Information Technology Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Thuckalay Kumaracoil India

Globally, wheat is a staple food crop, which plays a vital role in food security and sustenance for millions of people. However, the diseases in wheat crops affect their growth, productivity, and crop quality. Hence, early and timely disease detection is significant to prevent the loss of productivity and the economy. This article introduces an innovative hybrid model combining Moth Flame Optimization (MFO) and Radial Basis Functional Neural Network (RBFNN) for the prediction of disease wheat leaves from healthy ones. Initially, the wheat leaf image containing the normal and diseased leaves is collected and utilized as input for the system. Then, the dataset was filtered and related leaf attributes are tracked using the Histogram of Oriented Gradients. Subsequently, MFO fitness was employed to select the most informative leaf images from the extracted feature set. By utilizing these selected features, the RBFNN was trained to classify the normal and healthy leaves accurately. This model was evaluated with the wheat leaf dataset and the performances are evaluated. The presented model achieved greater accuracy 99.32%, recall of 99.33%, f-measure of 99.33%, and precision of 99.33%. The implementation outcomes exhibit that the designed framework has great potential for practical application in agriculture. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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A Review on Reconfigurable Antenna and its Design Methods for Sub-6 GHz Applications

A Review on Reconfigurable Antenna and its Design Methods fo...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Bhickta, Shalini Jaglan, Naveen Kumar, Amit Jaypee University of Information Technology Department of Electronics and Communication Engineering Solan Waknaghat India Jaypee University of Information Technology Department of Computer Science and Engineering Solan Waknaghat India

ISBN: (纸本)9798350364088

In modern wireless tele- communication, the key element for better and higher performance of the communication system is an Antenna. An antenna which removes all complex issues of weather, scattering and effects of multiple paths is in demand for current wireless system issues. The Antenna with Reconfiguration capabilities in current scenario solves these issues. The popularity of Reconfiguration in antennas has seen a significant growth due to its versatile capabilities. Therefore, reconfiguration in the structure of an Antenna without changing the physical dimensions and saving space realize the goal of high speed, gain, channel capacity and signal-to-Noise Ratios (SNR). In this paper a brief knowledge of various methods of designing a reconfiguration antenna is reviewed. The different switching techniques used for the activating the reconfiguration mechanism is discussed. The advancement of reconfiguration in multi-input multi-output (MIMO) systems is also studied. © 2024 IEEE.

关键词： Signal to noise ratio

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

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Recent Advances in computer Science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham Science Publishers.

关键词： Deep learning

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The Light Intensity Control System and User Distance Detection on the Laptop Stand 2

The Light Intensity Control System and User Distance Detecti...

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2nd International Symposium on information technology and Digital Innovation, ISITDI 2024

作者： Putri, Rahmi Eka Azkha, Naila Yolanda, Desta Universitas Andalas Faculty of Information Technology Department of Informatic Padang Indonesia Universitas Andalas Faculty of Information Technology Department of Computer Engineering Padang Indonesia

ISBN: (纸本)9798350389241

The use of laptops is now increasingly widespread. However, many still need to realize that using laptops with inappropriate attitudes and lighting can cause fatigue and damage to the eyes. Using a laptop for a long time can cause overheating, which will affect the laptop's performance. Therefore, in this study, a system has been created in the form of a multifunctional laptop stand that warns the user to adjust the distance from the laptop to a minimum of 46cm using an ultrasonic sensor. This system is also capable of measuring and meeting light requirements when using a laptop with a BH1750 sensor and PID control to control the intensity of light emitted by the LED with a setpoint value of 200 lux. Another capability of this system is to cool the laptop using a dc fan based on the temperature detected by the DHT22 sensor with a maximum laptop temperature of 35°C. Determination of the PID constant using the trial-and-error method with the results of Kp = 0.35, Ki = 0.18, and Kd = 0.05. The research results from the design and functional system work well in carrying out all its functions, so overall, it shows that the success rate reaches 100%. © 2024 IEEE.

关键词： Ultrasonic sensors

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Enhanced Speaker Accent Classification: An Experimental Study of Diverse Machine Learning Methods 1

Enhanced Speaker Accent Classification: An Experimental Stud...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, R.N. Gebremariam, Selam Habtewold Sivakumar, N. Komala, G. Alla, Poorna Chandra Reddy Chauhan, Shailendrasinh Marwadi University Department of Computer Engineering Rajkot India Marwadi University Department of Information Technology Rajkot India

ISBN: (纸本)9798331529246

Speaker Accent Recognition is a supervised learning task where the objective is to predict a speaker's dialect from an audio signal. This research examines linguistic features like pronunciation and intonation to classify speakers based on their unique speech patterns. Three hundred and thirty records in our UCI dataset show six different accents: spoken English from Spanish, German, Italian, French, and both US and UK accents. The model we proposed employs Mel-Frequency Cepstral Coefficients (MFCCs) to transform raw audio into numerical data, enabling the prediction of the speaker's accent within our classification categories. Various machine learning techniques are tested such as K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, Logistic Regression, and Naive Bayes, all with hyperparameter tuning. Results show that Random Forests achieved the highest accuracy at 95.62%, while Naive Bayes had the lowest at 76.09%. After tuning, SVM, Random Forests, and KNN reached accuracies of 97.31%, 95.96%, and 94.61%, respectively. The study highlights the importance of diverse algorithms and optimization techniques for effective accent recognition. © 2025 IEEE.

关键词： Random forests

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