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Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

Evaluating the Inclusiveness of Artificial Intelligence Soft...

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Artificial Intelligence, computer, Data Sciences and Applications (ACDSA), International Conference on

作者： Vasileios Alevizos Ilias Georgousis Akebu Simasiku Antonis Messinis Sotiria Karypidou Dimitra Malliarou Department of Learning Informatics Managament and Ethics Karolinska Institute Stockholm Sweden Department of Computer Science MLV Research Group International Hellenic University Kavala Greece Department Of Computer Software Engineering Zambia University Ndola Zambia HEDNO SA (Hellenic Electricity Distribution Network Operator) Department of Computer Science MLV Research Group International Hellenic University IntelliSolutions Athens Greece

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficien

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Applications of Machine Learning Techniques in Detecting Skin Cancer 5

Applications of Machine Learning Techniques in Detecting Ski...

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5th International Conference on Contemporary Computing and informatics, IC3I 2022

作者： Kunekar, Pankaj Vaishnav, Rounak Kalaivani, E. Gangodkar, Durgaprasad Kaur, Chamandeep Dhanraj, Joshuva Arockia Vishwakarma Institute of Technology Pune India Mahatma Gandhi University of Medical Sciences and Technology Clinical Research Manager Department of Medical Oncology Sri Ram Cancer and Super Specialty Centre Jaipur India Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Jazan University Department of Computer Science & Information Technology Saudi Arabia Department of Mechatronics Engineering Padur Chennai India

ISBN: (纸本)9798350398267

The most lethal kind of skin cancer is called malignant melanoma. Accurate diagnosis of skin malignancies are aided by dermoscopy, a non-invasive high-resolution imaging procedure. Melanoma is a very dangerous kind of skin cancer because of how quickly it spreads and how aggressive it is. For this reason, malignant melanoma maintains its place as one of the tumors with the highest rates of growth across the globe. Second-order texture feature data may be obtained by the GLCM (Grey Level Co-occurrence Matrix) technique. The reference pixel and neighboring pixels are considered by GLCM to ensure an accurate calculation. Grayscale pictures have a decent number of levels, which is straightforwardly relative to the number of segments and columns in the frameworks used to characterize the scale. Relative recurrence is addressed by the grid component P I j | Dx, Dy), where I and j are force values and Dx and Dy are pixel distances. In total, the co-occurrence matrix considers 14 different attributes, including energy, efficiency, latency, correlation, homogeneity, and contrast. Convolutional Neural networks (CNN) and Gaussian Filters (GF) classifiers were then used to sort these vectors. Comparisons of the results these classifiers provide are also made. © 2022 IEEE.

关键词： Pixels

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Sensor Based Smart Wearable Devices for Women: A Review 2

Sensor Based Smart Wearable Devices for Women: A Review

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Khadse, Chinmaya Hajare, Shital Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Natural organic menstrual cycles stay in large part taboo during many regions of India mainly rural areas hence threatening teenagers with useless menstrual hygiene requirements mixed with primary sanitation issues and network disapproval. The fitness problems and psychological misery get up from these subjects. The World Health Organization (WHO) establishes that inadequate menstrual hygiene ends in urinary tract infections and infections of the reproductive device. The importance of India's menstrual population now stands at 355 million girls and ladies which needs immediately reaction through powerful answers. This observe investigates how IA and IoT-driven smart wearable generation can increase menstrual health control (MHM) for teenage Indian women. A proposed low cost wearable tool collects basal body temperature (BBT) and heart charge variability (HRV) information and bodily activity information while retaining cultural appropriateness. AI chatbots and academic guide and actual-time comments capabilities exist inside this device. The system features complete menstrual health tracking competencies with the aid of using cycle prediction competencies and implements in-tool heating structures for ache discount together with strain reduction software for emotional aid. The system uses techniques that start from person desires even as imposing LSTM networks together with Random Forest Regression machines for forecasting menstrual cycles and symptom reviews. Validations run on the tool arise in practical conditions which observe both its performance accuracy along person pleasure quotes and reliability through the years. The observe shows wearable era has the capacity to revolutionize menstruation take care of Indian teens via presenting them with a powerful approach to address their public health needs. © 2025 IEEE.

关键词： mHealth

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Retraction Note: Resnet-based deep learning multilayer fault detection model-based fault diagnosis

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Multimedia Tools and Applications 2024年第38期83卷 86323-86324页

作者： Jaber, Mustafa Musa Ali, Mohammed Hasan Abd, Sura Khalil Jassim, Mustafa Mohammed Alkhayyat, Ahmed Majid, Mohammed Sh. Alkhuwaylidee, Ahmed Rashid Alyousif, Shahad Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Department of Computer Science Al-Turath University College Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Najaf Iraq Directorate of Research and development Ministry of Higher Education and Scientific Research Baghdad Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi University Baghdad Iraq College of Technical Engineering The Islamic University Najaf Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Technical Engineering Mazaya University College Thi Qar Iraq Department of Electrical and Electronic Engineering College of Engineering Gulf University Sanad Kingdom of Bahrain Research Centre University of Mashreq Baghdad Iraq

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Regression Models for Predicting Gamified Educational Outcomes

Regression Models for Predicting Gamified Educational Outcom...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ibrahim, Abdelhameed Gatzoulis, Christos Eid, Marwa M. Rizk, Faris H. Abualigah, Laith El-Kenawy, El-Sayed M. Isa Town Bahrain Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Applied Science Private University Applied Science Research Center Amman11931 Jordan Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan

ISBN: (纸本)9798350351408

This paper focuses on improving learning and involvement in studies by employing game elements and considering online learning platforms to deliver statistics curriculum. According to the problem statement, the research examines gamification methods to transform studying from a routine to having enjoyment while improving academic results. This area of investigation is being extended to predict academic outcomes by applying different machine learning models to student performance data received from gamified. The strategy entails using a pedagogy based on collecting data from students who participated in a gamified educational platform;the data comprises indicators like pre-and post-platform exam grades, grades on quizzes, and indicators of platform usage. Multiple regression algorithms, including Random Forest and Gradient Boosting, are applied to student performance. In the experiments, the output results indicate that the model of Random Forest Regressor outperforms other models with the least Mean Squared Error (MSE) of 0.052. This model performs the best in terms of prediction accuracy with competence level in predicting students' results in gamified environments. The findings can support decision-making in designing educational activities. The Random Forest Regressor model can be identified as the best predictive model and may help train gamified learning platforms, which can encourage student engagement that correlates to performance. © 2024 IEEE.

关键词： Prediction models

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Dynamics of cold random hyperbolic graphs with link persistence

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Physical Review E 2022年第6期106卷 064312-064312页

作者： Sofoclis Zambirinis Harrison Hartle Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Network Science Institute Northeastern University Boston Massachusetts 02115 USA

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω∈[0,1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties.

关键词： Evolving networks Random graphs

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Maximum Correntropy Criterion Kalman Filter for Indoor Quadrotor Navigation under Intermittent Measurements

Maximum Correntropy Criterion Kalman Filter for Indoor Quadr...

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Mediterranean Conference on Control and Automation (MED)

作者： Loizos Hadjiloizou Evagoras Makridis Themistoklis Charalambous Kyriakos M. Deliparaschos Department of Intelligent Systems KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Electrical and Computer Engineering and Informatics Department Cyprus University of Technology Limassol Cyprus

We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.

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Harnessing Data Mining for Improved Hindi Isolated Speech Recognition

Harnessing Data Mining for Improved Hindi Isolated Speech Re...

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Futuristic Technologies in Control Systems & Renewable Energy (ICFCR), International Conference on

作者： Ayasha Malik Veena Parihar Shrikant Mapari Malathy Sathyamoorthy Shilpa Saini Department of CSE IIMT College of Engineering (AKTU) Greater Nida India Symbiosis Institute of Geo-informatics Symbiosis International (Deemed University) Pune India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Technology KPR Institute of Engineering and Technology Coimbatore India Chandigarh University Mohali

ISBN: (数字)9798331504601

ISBN: (纸本)9798331504618

Speech is the primary medium for the exchange and transfer of messages, making recognition of speech an extensive area of study. The phonic and linguistic outline is probable for this, mostly for the English verbal. In India, a considerable portion of the population faces challenges to communicate in English. Henceforward, English speech detection is not useful for such persons. The recognition method of Hindi words is an integral segment of the automatic speech detection technique that has been proposed in this paper. The primary objective of Automatic Speech Recognition (ASR) is to recognize and interpret spoken input from a device or microphone and convert it into a textual document to execute the appropriate action. The procedure of detecting speech solely relies on the abstraction of attributes and the detection of patterns within the content. This research paper employs the Hidden Markov Model (HMM) along with the Hidden Markov Toolkit (HMT) and Pratt software for attribute detection. In the proposed scheme, a set of data is apprehended having 10 Hindi words: from “Shunya” to “Nau” (Shunya, Ek, Do, Teen, Chaar, Paanch, Chae, Saat, Aath, Nau) from the 15 voices of different age group then applying training along with testing over the set of data. The projected structure computes and compares the Linear Predictive Coding (LPC), Perceptual Linear Prediction (PLP), and Mel Frequency Cepstral Coefficients (MFCC) algorithms. These algorithms are applied to talker-reliant ASR and talker-free ASR in sound-dependent and word-based environments to figure out the top feature mining technique which is MFCC in our proposed system.

关键词： Voice activity detection Training Renewable energy sources Hidden Markov models Speech recognition Prediction algorithms Software Data mining Mel frequency cepstral coefficient Testing

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Predictive Analytics for Hospital Readmissions Using Logistic Regression and IoT Sensor Data 6

Predictive Analytics for Hospital Readmissions Using Logisti...

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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Kiruthiga, B. Riazulhameed, Arshadh Ariff Mohamed Abuthahir Rajapriya, M. Sarasu, R. Mohankumar, N. Kasthuri, A. Srm Valliammai Engineering College Department of Management Studies Tamil Nadu Chennai India Blackstraw Technologies Pvt Ltd Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Spatial Informatics Tamil Nadu Chennai India Maharashtra Nagpur India Vinayaka Mission's Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350379297

Hospital readmissions affect healthcare systems globally, increasing costs and morbidity. Readmissions must be predicted and prevented to improve patient outcomes and save healthcare costs. This research leverages the Internet of Things (IoT) sensor data and Logistic Regression (LR) to reduce hospital readmissions using predictive analytics. Patient health data from IoT devices during and after hospitalization is used in the paper. Heart rate, blood pressure, and oxygen saturation are constantly monitored. LR is used to examine this dataset and create a prediction model. The LR algorithm trains us to recognize IoT sensor data patterns and correlations that indicate readmission risks. Our methodology provides developed risk estimates incorporating patient-specific parameters, including age, multiple illnesses, and medical history. The temporal data permits dynamic predictions, considering patient health changes over time. This research detected hospital readmission risk with promising accuracy. These predictions allow healthcare practitioners to proactively intervene and deliver targeted treatments, including prescription modifications, lifestyle advice, and follow-up consultations to decrease readmission. This proactive strategy may improve patient outcomes and save healthcare expenses. © 2024 IEEE.

关键词： Logistic regression

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Enabling light coupling between nanowires and low refractive index contrast optical waveguides towards scalable quantum circuits 28

Enabling light coupling between nanowires and low refractive...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： Tsintzos, Symeon I. Tsimvrakidis, Konstantinos Sinani, Alexandra Gates, James C. Elshaari, Ali W. Smith, Peter G. R. Zwiller, Val Riziotis, Christos Theoretical and Physical Chemistry Institute National Hellenic Research Foundation Athens11635 Greece Department of Informatics and Computer Engineering University of West Attica Egaleo12243 Greece Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Stockholm106 91 Sweden Institute for Advanced Modelling and Simulation University of Nicosia Nicosia2417 Cyprus Qubitech Quantum Technologies Chalandri Athens15231 Greece

ISBN: (纸本)9781510670389

A primary constraint in the major photonic integration platform of Silica-on-Silicon, especially when combined with fabrication approaches like Direct Laser Writing is the optical waveguides' low refractive index contrast, leading thus to limitations for efficient coupling with currently available state-of-the-art single photon emitters such as semiconductor nanowires with quantum dots (NWQD). We propose and demonstrate a novel approach to drastically enhance the light coupling between silica based Laser-written channel waveguides and NWQDs, by incorporating an optical microsphere in their intermediate space. It is demonstrated that the induced photonic nanojet action of a suitably designed microsphere illuminated by the NWQD, excites efficiently the channel waveguide's modes and can enable light coupling to a degree even above 50%. The proposed method is reasonably robust to imperfections and misalignments and could be implemented by current state-of-the-art micro/nano patterning techniques. It is anticipated that the practical implementation of the method will allow the integration of multiple quantum emitters in silica based hybrid integrated circuits thus enabling their scalability towards for quantum computing and sensing applications. © 2024 SPIE.

关键词： Silica

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