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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Uma, J. Shobana, M. Abhiniti, G. Rahul, R. Kavinkumar, S.P. Hirithik Roshan, S. M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India

ISBN: (纸本)9798350349221

The IoT based Smart farmland using deep learning is a system for tracking animals on agricultural land combines surveillance cameras, drones, an Arduino controller, IR sensors, and an LCD display to detect and tally the animal's presence. Upon detection, the system utilizes AI methods to categorize the animal type and send immediate SMS notifications to farmers through a GSM module. It incorporates an animal-repellant buzzer and a gate control mechanism. Through the use of transfer learning with CNN models, it accurately identifies four specific animal species: elephants, cows, goats, and pigs. Continuous updates and training with new data maintain its precision, serving the agricultural and wildlife protection domains. This technology significantly aids farmers in safeguarding their crops, offering crucial insights into intrusions and amplifying crop yield. Its primary purpose revolves around simplifying the identification of animals on agricultural land. © 2024 IEEE.

关键词： Crops

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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Enhancing Anomaly Detection in Agriculture by Considering the Impact of Concept Drift: An Exploratory Study Using Digital Replica 23

Enhancing Anomaly Detection in Agriculture by Considering th...

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5th World Symposium on Software Engineering, WSSE 2023

作者： Huo, Dongyang Malik, Asad Waqar Ravana, Sri Devi Ur Rahman, Anis Ahmedy, Ismail Department of Information Systems Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Islamabad Pakistan Department of Computer Systems and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (纸本)9798400708053

The advent of precision agriculture is disrupting traditional farming practices, leading to a significant increase in the data flow. However, current anomaly detection methods overlook the impact of concept drift. The phenomenon of concept drift refers to the changes in the underlying distribution of the data over time, which can result in unexpected and misleading results. As such, it is essential to consider the impact of theconcept drift in the context of anomaly detection in precision agriculture. This study uses a digital replica of an agricultural system equipped with intelligent agents to examine the system dynamics and the temporal variability of end-to-end data flow in precision agriculture. It highlights the need for robust methods that can effectively address the challenges posed by concept drift and systems’ dynamism enabling accurate and reliable anomaly detection in the IoT farming system. © 2023 Copyright held by the owner/author(s).

关键词： Precision agriculture

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Knowledge Leaks in Data-Driven business Models? Exploring Different Types of Knowledge Risks and Protection Measures

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Schmalenbach Journal of business Research 2024年第3期76卷 357-396页

作者： Fruhwirth, Michael Pammer-Schindler, Viktoria Thalmann, Stefan Know-Center GmbH Institute for Interactive Systems and Data Science Faculty of Computer Science and Biomedical Engineering Graz University of Technology Silicon Austria Labs GmbH Business Analytics and Data Science-Center (BANDAS-Center) School of Business Economics and Social Sciences University of Graz

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive knowledge by sharing data-related value objects, such as data, models or predictions. Although knowledge risks have been studied in traditional business models, little research has been conducted in the direction of data-driven business models. In this explorative qualitative study, we conducted 28 expert interviews in three rounds (two exploratory and one evaluatory) and identified five types of risks along the three basic types of value objects: data, models and predictions. These risks depend on the context, i.e., when competitive knowledge could be discovered from shared value objects. We found that those risks can be mitigated by technology, contractual regulations, trusted relationships, and adjusting the business model design. In this study, we show that the risk of knowledge leakage is a relevant risk factor in data-driven business models. Overall, knowledge risks should be considered already during business model design, and their management requires an interdisciplinary approach via a balanced assessment. The level of knowledge protection from a technology perspective highly depends on computer science innovations and thus is a moving target. As an outlook, we suggest that knowledge risk will become even more relevant with the extensive usage of machine learning and artificial intelligence in data-driven business models. © The Author(s) 2024.

关键词： business model innovation Data analytics Data-driven business models Knowledge risks Risk management Value objects

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Responding to platform owner moves: A 14-year qualitative study of four enterprise software complementors

Responding to platform owner moves: A 14-year qualitative st...

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作者： Kude, Thomas Huber, Thomas L. Faculty of Information Systems and Applied Computer Sciences University of Bamberg Germany Department of Information Systems Decision Sciences and Statistics ESSEC Business School Cergy France

Integrating the competition and the cooperation perspectives on value co-creation in platform ecosystems, this study explores complementor responses to platform owner moves that adversely affect the complementor's positioning. Our primary focus is to discern dynamic patterns of complementor responses and to understand their role in the process of re-stabilising the complementor's positioning. To this end, we conducted a 14-year longitudinal study, analysing 21 move-response instances across four platform partnerships. Our findings reveal three distinct complementor response archetypes: insist, pivot, and detach. Over time, complementors combine these archetypes into three unique response patterns. Through progressive diverging, complementors sidestep platform owner moves by diversifying their offer beyond the focal ecosystem. Although this can entail substantial multi-homing costs, it reduces dependence on the platform owner and bolsters resilience against future moves. Through adaptive oscillating, complementors use platform owner moves as opportunities to gradually diversify their offer within the focal ecosystem. This stepwise market expansion makes them resilient against future moves, while mitigating costs and efforts related to multi-homing. Through persistent insisting, complementors can re-establish their previous positioning, but at the cost of increased dependence on the platform owner, leaving the complementor vulnerable to future moves. We synthesise these findings in our process model of complementor positioning. Emphasising the importance of complementor responses in fostering resilient software ecosystems, this model bears important implications for research on platform governance and platform-dependent entrepreneurship. © 2024 The Authors. Information systems Journal published by John Wiley & Sons Ltd.

关键词： Ecosystems

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Exploring the Role of Sleep Quality, Obsessive–Compulsive Disorder, Fear of Missing Out, and Psychological Resilience on Problematic Instagram Use

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Journal of Technology in Behavioral science 2024年 1-11页

作者： Yilmaz, Ramazan Avci, Ummuhan Karaoglan Yilmaz, Fatma Gizem Department of Computer Technology & Information Systems Faculty of Science Bartin University Bartin Turkey Department of Management Information Systems Faculty of Economics and Administrative Sciences Bartin University Bartin Turkey

This study addresses a gap in the literature regarding the relationships between sleep quality, obsessive–compulsive disorder (OCD), fear of missing out (FoMO), psychological resilience, and problematic Instagram use (PIU) among university students. By examining these interrelated factors, the research aims to clarify the mechanisms behind Instagram addiction, support effective interventions, and contribute to academic knowledge. This study examined university students’ sleep quality, PIU, OCD, FoMO, and psychological resilience, as well as the structural relationships between these variables. The study encompassed a sample of 407 Turkish university students with an average age of 21.78. The age range of the participants was between 18 and 35 years;43% of them were female, and 57% were male. This study was conducted according to the correlational research method, and pathway analysis was used to conduct the research. Research findings show that OCD and FoMO positively affect PIU. In other words, as OCD and FoMO increase, Instagram addiction also increases. Psychological resilience has an indirect effect on PIU. OCD plays a mediating role between psychological resilience and PIU and between psychological resilience and FoMO. Therefore, as psychological resilience increases, PIU and FoMO decrease. A positive correlation was found between OCD and FoMO. As the OCD level increases, the FoMO level of the individuals also increases. Another study finding was that OCD, FoMO, psychological resilience, and PIU did not significantly affect sleep quality. Future research may focus on exploring cause-effect relationships among these variables alongside implementing school-based programs to enhance students’ psychological resilience through training, planning, and follow-up studies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Fear of missing out (FoMO) Obsessive–compulsive disorder Problematic Instagram use Psychological resilience Sleep quality

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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 3895-3920页

作者： Abdul Haseeb Muhammad Attique Khan Majed Alhaisoni Ghadah Aldehim Leila Jamel Usman Tariq Taerang Kim Jae-Hyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirut1100Lebanon College of Computer Science and Engineering University of Ha’ilHa’il81451Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428RiyadhSaudi Arabia Department of Management Information Systems College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Diagnosing gastrointestinal cancer by classical means is a hazardous *** have witnessed several computerized solutions for stomach disease detection and ***,the existing techniques faced challenges,such as irrelevant feature extraction,high similarity among different disease symptoms,and the least-important features from a single *** paper designed a new deep learning-based architecture based on the fusion of two models,Residual blocks and Auto ***,the Hyper-Kvasir dataset was employed to evaluate the proposed *** research selected a pre-trained convolutional neural network(CNN)model and improved it with several residual *** process aims to improve the learning capability of deep models and lessen the number of ***,this article designed an Auto-Encoder-based network consisting of five convolutional layers in the encoder stage and five in the decoder *** research selected the global average pooling and convolutional layers for the feature extraction optimized by a hybrid Marine Predator optimization and Slime Mould optimization *** features of both models are fused using a novel fusion technique that is later classified using the Artificial Neural Network *** experiment worked on the HyperKvasir dataset,which consists of 23 stomach-infected *** last,the proposed method obtained an improved accuracy of 93.90%on this *** is also conducted with some recent techniques and shows that the proposed method’s accuracy is improved.

关键词： Gastrointestinal cancer contrast enhancement deep learning information fusion feature selection machine learning

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A Hybrid Flood Forecasting Approach Using Hidden Markov Model and Machine Learning Techniques 7

A Hybrid Flood Forecasting Approach Using Hidden Markov Mode...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sri Sakthi, L. Chitra, P. Harini Sree, M.S. Subbulakshmi, P. Umayal, C. Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350304268

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. The amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. Machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. The proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. The developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August. © 2023 IEEE.

关键词： Flood prediction Hidden Markov Model(HMM) Hybridization Machine Learning (ML) Rainfall Temperature

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Energy and QoS-aware virtual machine placement approach for IaaS cloud datacenter

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Neural Computing and Applications 2025年第4期37卷 2211-2237页

作者： Elsedimy, E.I. Herajy, Mostafa Abohashish, Sara M. M. Department of Information Technology Management Faculty of Management Technology and Information Systems Port Said University Port Said Egypt Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt Cyber Secuirty Department Faculty of Science Al-Esraa University College Baghdad Iraq

Virtualization technology enables cloud providers to abstract, hide, and manage the underlying physical resources of cloud data centers in a flexible and scalable manner. It allows placing multiple independent virtual machines (VMs) on a single server in order to improve resource utilization and energy efficiency. However, determining the optimal VM placement is crucial as it directly impacts load balancing, energy consumption, and performance degradation within the data center. Furthermore, deciding on VM placement based on a single factor is usually insufficient to improve data center performance because many factors must be considered, and ignoring them may be too expensive. This paper improves a new multi-objective VM placement (MVMP) algorithm using a quantum particle swarm optimization (QPSO) technique. We call it QPSO-MOVMP, and its objective is to find the Pareto optimal solution for the VM placement problem by balancing different goals. This algorithm generates Pareto optimal solutions that save power by minimizing the number of running physical machines, avoid performance degradation by maintaining service level agreement (SLA), and improve load balancing by keeping server loads at optimal utilization. The experimental results show that QPSO-MOVMP had superior performance in terms of power consumption and performance degradation compared to three other multi-objective algorithms and three conventional single-objective algorithms. Simulation results show that the proposed QPSO-MOVMP achieves a consumption of 2.4 × 104 watts in power. Furthermore, it outperformed the others, achieving a minimum of 12% SLA breaches while experiencing a significant surge in requests from VMs. Moreover, the proposed model generated Pareto solutions that had a better distribution than those derived from a comparative method. © The Author(s) 2025.

关键词： Particle swarm optimization (PSO)

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