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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Ines, Joefrelin C. Tolentino, Marilou Q. Cruz, Jay C. Dela Estabillo, Rolando Madlambayan, Anthony M. Mirano, Cirilo E. Vergara, Juvy A. College of Economics and Business Administration University of Technology and Applied Sciences Department of Business Admnistration Shinas Oman College of Business Systems Plus College Foundation Angeles City Philippines College of Computing and Information Sciences University of Technology and Applied Sciences Department of It Shinas Oman

ISBN: (纸本)9798331542573

This study investigates the transformative role of artificial intelligence (AI) in enhancing decision-making processes within small and medium enterprises (SMEs). By focusing on AI applications such as predictive analytics, the research highlights how these tools improve operational efficiency, optimize resource management, and facilitate real-time decision-making. Employing a quantitative methodology, data was collected from 100 SMEs in the Angeles City, Philippines through structured surveys, which were analyzed using descriptive statistics, regression, and correlation techniques. The findings reveal that AI significantly enhances decision-making processes, leading to improved business outcomes, including increased operational efficiency and profitability. Notably, 75% of the surveyed SMEs reported using predictive analytics, resulting in heightened decision- aking speed and accuracy. This study underscores the critical importance of AI in empowering SMEs to navigate market challenges effectively and sustain competitive advantages in an increasingly data-driven business landscape. © 2024 IEEE.

关键词： Artificial Intelligence Business Performance Decision-Making Operational Efficiency Predictive Analytics Resource Optimization Small and Medium Enterprises

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Development of Automated Techniques for Object-Oriented Image Analysis in Hyper Spectral Images

Development of Automated Techniques for Object-Oriented Imag...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Abrol, Monika Pandey, Rajendra P. Pawar, Rahul Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

the development of computerized strategies for item-orientated image evaluation in Hyper Spectral photos (HSI), an emerging field of applying machine-gaining knowledge of synthetic intelligence, has ended up an increasing number of crucial in a selection of domain names. This kind of analysis calls for a particular and correct illustration of the gadgets of interest from the hyperspectral photos. For this reason, characteristic extraction, classifiers, and clustering techniques have been proposed if you want to come across and classify them greenly. The maximum, not unusual feature extraction techniques used to extract statistics from HSI consist of radiometry, spectral band shapes, and spectral correlation. These function extraction strategies produce specific characteristic descriptors that can be utilized in aggregate with item classifiers and clustering solutions to detect and classify the objects gift in the HSI. Characteristic extraction strategies, together with Radiometric Normalized distinction flora Index (NDVI) and significant components analysis (PCA), have been observed to achieve success in numerous scenarios. Classifiers, linear and nonlinear SVM, neural networks, and choice bushes are the most famous strategies for reading HSI. Using a single this kind of strategy has been seen to offer the most straightforward restricted outcomes;however, using a combination of those strategies has been visible to enhance the classification performance. © 2024 IEEE.

关键词： characteristic classification development function strategies

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Exploring the Potential of Recurrent Neural Networks for Medical Image Segmentation

Exploring the Potential of Recurrent Neural Networks for Med...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Jain, Aaditya Mandal, Sanjeev Kumar Abrol, Monika Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Recurrent Neural Networks (RNNs) are a modern-day state-of-the-art algorithm that is brand new modern getting used for clinical picture segmentation. RNNs are, in particular, nicely applicable for this undertaking due to the fact they can be skilled to bear in mind patterns over long sequences brand new information. This enables them to perceive structural patterns in an image and carry out sophisticated segmentation obligations together with tumor or organ boundary identification. similarly, RNNs have the ability to contain earlier know-how from different pics and medical data, as well as contextual know-how from external resources such as electronic fitness information. This paper critiques the contemporary in RNNs for medical picture segmentation, outlining the key methods and programs contemporary RNNs inside the field. We discuss both the successes and demanding situations of trendy RNN-based procedures and provide destiny studies directions for the improvement of modern-day extra correct and efficient segmentation equipment. © 2024 IEEE.

关键词： development directions outlining segmentation incorporate

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AI Modeling and Water Quality Sensing Technique Proffers Water Security: An Open Review 3

AI Modeling and Water Quality Sensing Technique Proffers Wat...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Wilfred Blessing, N.R. Palarimath, Suresh Gunasekaran, Hemalatha Haidar, Sk Wasim Sutherlin Subitha, G. Sarmila, J. University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari District India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Chunkankadai India

ISBN: (纸本)9798331509675

Water Quality Sensors (WQSs) are becoming a promised tool in water quality data assessment and scientific value of aquatic structure. Such sensors are broadly used to produce live results by evaluating major water quality parameters and components. Artificial intelligence (AI) based WQSs and machine learning (ML) algorithms primarily enable long term operations for real time detention of water quality information at different time periods. Messaging services, alarms, alerts and other proper warnings are given to water quality managers or assessing communities while the quality deterioration occurs at the particular water quality checking sites. A good number of impressive challenges are identified in hosting the variety of sensors to manage, warehouse and analyze incessant water quality information that they generate periodically. This paper places interests in highlighting lack of suitable assessment standards and determine logistical deliberations for fruitful implementations together with transportation, storage services and appropriate trustworthy communication. The main objective is to identify the designs targeting water security that support to monitor, reassure and protect the water resources all the way through uninterrupted quality supervision in addition to AI features. This open review also discusses a variety of applications in water quality sensors, AI modeling, electronic tongues and indices, hence results the knowledge of improvement required for upgrading the quality in water resources through computer technologies and models. © 2025 IEEE.

关键词： Electronic tongues

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Enhancing Medical Image Segmentation Through Knowledge Transfer

Enhancing Medical Image Segmentation Through Knowledge Trans...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Raghavendra, R. Abrol, Monika Tripathi, Ramesh Chandra Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Clinical image segmentation is a challenge that identifies a selected organ or anatomical structure in a given scientific image which can be of radiographic or other modality. Expertise switch is an essential side of clinical image segmentation as medical practitioners may additionally possess information about the patient's anatomy or physiology that could contribute to the segmentation process. Understanding transfer to a deep mastering version, such as a convolution neural community, can allow the model to perform correct segmentation compared to other tactics. In this paper, we explore strategies and packages of expertise switches for scientific photograph segmentation, discuss its blessings and challenges, and eventually advise a singular method based totally on understanding fusion. We increase a two-step framework to fuse photo-degree features and segmentation labels with medical data before segmentation. The proposed approach demonstrates advanced segmentation accuracy in assessing the present strategies... © 2024 IEEE.

关键词： comparison contribute important information segmentation

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Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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Data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data Science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands eScienceCenter AmsterdamNL Department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands Department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Automating Weed Detection Through Hyper Spectral Image Analysis

Automating Weed Detection Through Hyper Spectral Image Analy...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Bharadwaj, Shambhu Prabhu, A. Solanki, Vipin Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

关键词： accuracy crops harm measures promising

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance 46

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Horvat, M. Jercic, P. University of Zagreb Faculty of Electrical Engineering and Computing Department of Applied Computing Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

ISBN: (纸本)9789532331042

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors' intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers' opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey. © 2023 MIPRO Croatian Society.

关键词： Database systems

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Smart Traffic Control: Adaptive Signal Management Based on Real-time Lane Detection using YOLOv8

Smart Traffic Control: Adaptive Signal Management Based on R...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Ravichandran, M. Laxmikant, Kumar Muthu, Annamalai Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology & Applied Science Department of Electrical and Electronics Engineering Nizwa Oman

ISBN: (纸本)9798350382723

As cities grow, handling traffic in big urban are-as becomes a huge proble-m. More cars on the road and not enough roads le-ad to heavy traffic jams. This increases trave-l time and harms our environment. Our study tackle-s these problems with a ne-w approach. We use real-time- lane detection with YOLOv8 and adaptable- traffic lights. Using sharp computer vision, our system pinpoints vehicle-s. It adjusts traffic light timings on-the-go to improve traffic flow. Our model works e-xtremely accurately. During the- learning phase, it achieve-d a mAP50 score of 99.3% and a mAP50-95 score of 87.4%. In the te-sting phase, it got a mAP score of 99.2% and a mAP50-95 score of 86.2%. The re-sults highlight how the system can improve city trave-l. It's valuable for city planners and traffic officials. It helps the-m understand smart transportation systems bette-r. © 2024 IEEE.

关键词： Computer vision

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Deriving a Power-Law Model for Aggregating Data with Head Based Clustering

Deriving a Power-Law Model for Aggregating Data with Head Ba...

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2024 International Conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Solanki, Vipin Rastogi, Ajay Raghavendra, R. Sanskriti University Department of Applied Science Uttar Pradesh Mathura India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Head-based Totally Clustering is a technique of grouping facts and factors with similar traits using a rooted tree structure. The research paper Deriving an electricity-law version for Aggregating facts with Head primarily based Clustering outlines an energy-law model for creating clusters of facts points in a head-based totally hierarchy that may be used to efficiently mixture groups of related information. The authors used a topological distance technique to generate the clusters and implemented the method on an objective global dataset of web pages. The proposed model is designed to lessen the complexity of incorporating records from a pre-described tree shape with electricity regulation. The effects confirmed a widespread discount in computational time and aid utilization. Additionally, the approach could increase aggregated facts' accuracy by incorporating numerous information points in a head-based hierarchy. The look gives a similar understanding of the potential application of a strength-regulation model for aggregating statistics with head-primarily based Clustering. © 2024 IEEE.

关键词： aggregating computational Head based incorporating technique

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