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Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE. -
Facial Emotion Recognition Augmented with CNNs and Face Detection: Toward Emotive Emoji Synthesis
Emotion recognition is a crucial component with broad applications in technology and healthcare industries specifically in humancomputer interaction. To improve emotion recognition accuracy, this research introduces an innovative technique that integrates face detection with Convolutional Neural Networks (CNNs). Using the Fer2013 dataset, the approach consists of carefully identifying faces in images as a preprocessing step, followed by training a CNN network to identify emotions and create corresponding emojis. After conducting extensive testing and assessment, it is determined that after employing a face detection algorithm the suggested framework is effective in both correctly identifying emotions and producing visually appealing emojis. This helps to create an interface for emotional communication that is more user-friendly and captivating. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Millets Industry Dynamics: Leveraging Sales Projection and Customer Segmentation
Millets delves into the dynamics of the millets industry, with a particular focus on sales projection and customer segmentation as strategic levers for growth. The research commences with an in-depth analysis of the millets market, encompassing production patterns, consumption trends, and emerging market opportunities. It explores the diverse range of millets varieties, their nutritional profiles, and the factors driving consumer preference. By understanding the market landscape, the study identifies key trends and challenges shaping the industry. A core component of this research is the development of a robust sales projection model. Employing advanced statistical and data-driven techniques, the model forecasts future sales based on historical data, market trends, and relevant economic indicators. The model incorporates factors such as consumer demographics, purchasing behavior, and competitive landscape to provide accurate and actionable insights. Customer segmentation is another critical aspect of the study. By applying clustering and profiling methodologies, the research identifies distinct customer segments based on factors such as age, income, dietary preferences, and purchasing habits. This segmentation enables a deeper understanding of customer needs and preferences, facilitating targeted marketing strategies and product development. The integration of sales projection and customer segmentation empowers businesses to make informed decisions, optimize resource allocation, and enhance overall market performance. By aligning product offerings and marketing efforts with customer segments, companies can achieve higher customer satisfaction, increased market share, and improved profitability. This research contributes to the growing body of knowledge on the millets industry by providing valuable insights into market dynamics, sales forecasting, and customer segmentation. The findings offer practical guidance for industry stakeholders, including farmers, processors, retailers, and policymakers, in navigating the evolving millets landscape. By leveraging the potential of sales projection and customer segmentation, the millets industry can unlock new opportunities and achieve sustainable growth. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
A novel approach to optimize power utilization and scheduling in dynamic networks through generative adversarial network-based prediction of network parameters
The infrastructure-less network communication has been in an ever-increasing demand to cater to the needs of effective communication while the network dynamism exists. The quality of service (QoS)quality of service (QoS) demands increasing the efficiency of network by reducing the time taken for a data packet to reach the destination, increasing the probability of successful data transmissiondata transmission, minimizing packet loss,packet loss and optimizing power utilizationpower utilization. In this study, a generative adversarial network-based learning modelgenerative adversarial network-based learning model has been developed that considers the previous network statistics, as realized data, to predict future network patterns by the generatorgenerator to make such predictions, called as unrealized data, as near to the realized data. Further, the proposed model uses penalty-award criteria by the discriminatordiscriminator, to fine-tune the predicted network parameters. Now, having the set of realized and unrealized data, the model uses Markov decision processMarkov decision process to perform power scheduling and effective utilization of buffer space. The buffer utilization in the intermediate nodes necessitates the model to stochastically schedule the data transmission, depending on the percentage of utilization of buffer. Simulation results denote the effective utilization of buffer that makes continued transmission of data, whenever possible, without having data packet lossdata packet loss. Also, power scheduling, by the use of goodput function and increased transmission probability improves the power utilization that ultimately increases the lifetime of the network. 2026 Walter de Gruyter GmbH, Berlin/Boston, Genthiner Stra 13, 10785 Berlin. -
Smart Street Lighting System Using AI, Computer Vision, and IoT for Energy Efficiency and Sustainability
In pursuit of smart cities, optimum utilization of energy without any compromise on public security is a requirement. Traditional lighting systems work with minimal flexibility but result in enormous wastage of energy. The proposed model dynamically controls lighting intensity based on real-time pedestrian and environmental information. The model is capable of saving wasteful utilization of electricity by integrating innovative technologies without any compromise on public security and visibility levels. The system requires the deployment of IoT-enabled cameras at strategic points on the highways to monitor the movement and traffic of pedestrians and automobiles. The system is capable of detecting and recognizing moving objects in real-time through computer vision technologies. Based on movement and density patterns, the system determines the optimal level of brightness to be used over each street lighting segment. This adaptive lighting method ensures the utilization of energy only where it is required and minimizes the energy consumption substantially without any compromise on security. Overall, this AI-driven smart lighting solution is a cost-effective and scalable solution for cities to become intelligent and resource-efficient. 2025 IEEE. -
IoT-Based Emergency Vehicle Detection Using YOLOv8
The rapid response of emergency services plays a critical role in saving lives and minimizing the impact of emergencies. However, identifying and locating emergency vehicles in real-Time can be challenging, especially in congested urban areas. This paper focuses on the emergency vehicle identification using the You Only Look Once version 8 (YOLOv8) algorithm and is focused on Internet of Things (IoT). The goal of this research is to develop a real-Time and precise emergency vehicle detection system using You Only Look Once version 8 (YOLOv8) algorithm, trained and tested with a dataset from a camera placed on a busy road, to enhance emergency service response times. The findings demonstrate the suggested system's ability to recognize emergency vehicles at a speed of 31 frames per second and with a 95% accuracy rate. Modern object identification algorithms include the You Only Look Once version 8 (YOLOv8) algorithm, which has shown promising results in various applications. The proposed system is built on a Raspberry Pi, which acts as an edge device and processes the video stream in realtime. The system consists of an Internet of Things (IoT) device with a camera that captures the live video stream, which is then fed into the algorithm for object detection. Once an emergency vehicle is detected, the system sends an email notification to the nearby emergency services, like a police station, using Simple Mail Transfer Protocol (SMTP), who can then take appropriate action. The results of this investigation show that the Internet of Things and You Only Look Once version 8 (YOLOv8) algorithms have great promise for creating effective and dependable emergency vehicle detection systems. The proposed system possesses the capacity to save lives and improve the effectiveness of emergency response by speeding up response times for emergency services. The suggested solution is also inexpensive, simple to implement, and adaptable to existing infrastructure. Through the development of intelligent transportation systems, emergency services can operate more safely and effectively. More sophisticated machine learning algorithms may be incorporated into the proposed system, and further sensors can be added to utilize alternative methods beyond camera-based detection to identify emergency vehicles. Overall, this research shows the potential of Internet of Things (IoT) and machine learning in creating creative emergency services solutions. 2025 Syed Suhana et al.Published by Sciendo. -
Geometric aspects of noncommutative wormholes with conformal symmetry
This paper investigates traversable wormhole solutions within the framework of (Formula presented) f(Q,T) gravity by incorporating conformal symmetry and employing a Lorentzian distribution to model the matter sources. The study considers different equations of state, such as traceless and barotropic forms, to explore their impact on the viability of wormhole solutions. A comprehensive analysis of the effects of the model parameters on the wormhole geometry and its physical properties is carried out. The findings reveal that the resulting shape function satisfies all the necessary wormhole conditions. Importantly, certain scenarios are identified where the wormhole can be supported by non-exotic matter, highlighting the physical plausibility of such solutions within modified gravity. 2026 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This article is available under the terms of the https://publishingsupport.iopscience.iop.org/iop-standard/v1. -
Legacy of St. Kuriakose Elias Chavara and CMI and CMC congregations: educational modernity in Kerala
Education plays a predominant role in modernising society. It has the power to transform society by dismantling out-dated superstitions and promoting progressive social change. The present study explores the educational initiatives of St. Chavara and his followers, the CMI and CMC, and their role in shaping the modernity of nineteenth century Kerala, India. The study highlights how St. Chavaras visionary leadership in education contributed to modernisation in Kerala. The study focuses on his key initiatives such as secular education, inclusive education, women education, universal education and new educational support programmes which led to modernity of Kerala. The study reveals that St. Chavara's educational initiatives had a profound impact on both individuals and society of nineteenth century Kerala. The study emphasises the continued impact of St. Chavaras educational legacy through the institutions led by his followers, the CMI and CMC, which played a significant role in driving the modernisation of Kerala. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Vocational training for women empowerment: Saint Kuriakose Elias Chavaras vision
The present study attempted to describe the initiatives of Saint (St.) Kuriakose Elias Chavara on vocational training and womens empowerment. The study narrated the present condition of one of his vocational training initiatives known as rosary-making, which is in vogue at Koonammavu Village in Kerala state, India. The study employed a multi-method research design approach to carry out the present study. It included historical, qualitative, and quantitative methods in sequence. In the historical method, the study employed document analysis of primary and secondary sources. As a part of the qualitative method, the study conducted a semi-structured interview with 10 rosary-making entrepreneurs in Koonammavu. As a part of the quantitative method, the study administered a questionnaire to 100 families who are actively involved in the rosary-making business. Document analysis revealed that St. Chavaras initiative on rosary-making vocational training for nuns was a contribution to womens empowerment. Narrative thematic analysis revealed 5 main themes and 10 subthemes in Chavaras contribution. Quantitative data revealed that rosary-making emerged as a livelihood, business, and source of income for many families. The study recommends future researchers focus on all the initiatives of St. Chavara in the realm of vocational training and womens empowerment. 2025, Intelektual Pustaka Media Utama. All rights reserved. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Explaining Autism Diagnosis Model Through Local Interpretability Techniques - A Post-hoc Approach
In this era of machine learning and deep learning algorithms dominating the Artificial Intelligence (AI) world, the trustworthiness of these black box models is still questionable. Life-caring sectors like healthcare and banking make use of these black box models as assistance in critical decision-making processes, but the degree of reliability of these decisions is still uncertain. This is because these black box models will not reveal the causation of the predicted outcome. However, creating an interpretable model that can explain the internal workings of these black box models can provide some reliable insights and trustable justifications for the predicted outcome. This study aimed to create an interpretable model for autism diagnosis which can give some trustable explanations for its predicted outcome. Using local interpretability methods such as LIME, SHAP, and Anchors the predicted outcome for each instance is explained well with some standard visual representations. As a result, this study developed an interpretable autism diagnosis model with an accuracy rate of 91.37% and with good local model explanations. 2023 IEEE. -
Diagnosis of Autism Spectrum Disorder: A Review of Three Focused Interventions
Autism is a neurological developmental disorder that impacts a persons physical, social, and emotional behavior. This disorder develops over time and is characterized by social deficits and repetitive behavior. Although there is no cure for this disorder, an early diagnosis and intervention can do significant wonders and can help the subject to become active functioning members of the family and society. The aim of this study is to minimize the diagnostic period by finding an optimal diagnosis procedure from the existing diagnosis tools. The diagnosis of autism can be done in three ways: 1. clinical evaluation; 2. screening tools; 3. brain images. In this review paper, we have thoroughly gone through all three types of diagnostic procedures and found that there was no single diagnostic tool to confirm the disorder. We also found that the diagnosis period was too long. As the result of this review, we found an ASD diagnosis triad which helps to choose the right diagnosis procedure based on the subjects age which reduces the diagnostic period and helps to aid early diagnosis by eliminating the chaos in choosing the diagnostic tools. 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
The effect of spatial and intensity level augmentation of structural magnetic resonance images on autism diagnosis model
In deep learning, the robustness and generalizability of models significantly depend on diverse and heterogeneous training data. Acquiring such an extensive dataset is challenging in fields like disorder prediction due to data scarcity, which can be attributed to factors such as privacy concerns, limited patient population, or inadequate facilities. Data augmentation can be an ideal solution to this problem, particularly in the field of disorder prediction, like autism, using medical imaging. Data augmentation can expand and balance datasets by generating high-quality and varied data, thereby improving the generalizability of deep learning models. This study proposed two types of augmentation methods: 1. Spatial level 2. Intensity level augmentation techniques. Eight different levels of augmentations were experimented with across these categories. This study found that the combination of spatial and intensity level augmentations enhanced the model's generalizability and robustness, achieving an AUC value of 0.7433. Additionally, it was observed that the Left to Right flip method, under spatial augmentation, diminished the model's performance, whereas random noise injection, under intensity level augmentation, improved prediction accuracy. 2026 Elsevier B.V. -
FaithfulNet: An explainable deep learning framework for autism diagnosis using structural MRI
Explainable Artificial Intelligence (XAI) can decode the black box models, enhancing trust in clinical decision-making. XAI makes the predictions of deep learning models interpretable, transparent, and trustworthy. This study employed XAI techniques to explain the predictions made by a deep learning-based model for diagnosing autism and identifying the memory regions responsible for children's academic performance. This study utilized publicly available sMRI data from the ABIDE-II repository. First, a deep learning model, FaithfulNet, was developed to aid in the diagnosis of autism. Next, gradient-based class activation maps and the SHAP gradient explainer were employed to generate explanations for the model's predictions. These explanations were integrated to develop a novel and faithful visual explanation, Faith_CAM. Finally, this faithful explanation was quantified using the pointing game score and analyzed with cortical and subcortical structure masks to identify the impaired brain regions in the autistic brain. This study achieved a classification accuracy of 99.74% with an AUC value of 1. In addition to facilitating autism diagnosis, this study assesses the degree of impairment in memory regions responsible for the children's academic performance, thus contributing to the development of personalized treatment plans. 2025 Elsevier B.V. -
Rectifying Whole Brain Segmentation Errors Using a Novel Under-Segmentation Correction Method
Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Meta-Teaching on Leveraging the Metaverse for Definitive Efficiency in Learning in Higher Education
One of the most intriguing results of the technology revolution over the past ten years has been virtual reality (VR). This experience is set to be enhanced by the metaverse, the next major technological revolution of our time. The metaverse delivers a fully immersive 3D digital experience that blends virtual and real worlds. The idea is interpreted as the future of the internet, it will allow users to interact with one another in a 3D virtual environment, through gaming or collaborating on projects. In the education sector, metaverse will play a vital role in overcoming learning limitations. Activities that occur in remote locations in the real world can now take place virtually. With VR, students are fully immersed in a simulated environment, free from distractions which enhances the student's ability to learn. Scientific studies show that VR improves spatial memory and cognition. Visual learning can boost student's understanding of more complicated subjects, concepts and languages by allowing them to learn from a first-person perspective and observe everything happening around them. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Implementation of OpenId connect and O Auth 2.0 to create SSO for educational institutes
Increase in the number of users is directly proportional to the need of verifying them. This means that any user using any website or application has to be authenticated first; this leads to the creation of multiple credentials of one user. Now if these different websites or applications are connected or belong to one single organization like a college or school, a lot of redundancy of data is there. Alo ng with this, each user has to remember a wide range of credentials for different applications/websites. So in this paper, we addre ss the issue of redundancy and user related problems by introducing SSO using OpenId Connect in educational institutes. We aim to mark the di fference between the traditional system and proposed login by testing it on a group of users. 2018 Authors. -
Smart city initiatives and disaster resilience of cities through spatial planning in Pune city, India
Cities are attracting populations at alarming rate. Cities provide the need of populations in every way from livelihoods to livability. In doing so it is exhausting its resources resulting in increasing threats of risk. An initiative like Smart City Mission is aiming to enhance the capacities of the cities to increase livability and quality of life for its population and decrease threats of risk. This study examines the impact of smart city initiatives on resilience to earthquakes and floods through a spatial planning perspective for the city of Pune in State of Maharashtra through series of structured interviews with key stakeholders. The findings suggest that smart city initiative is still in its primary stage and requires assimilation with the development strategy to contribute to the resilience of the city. The study further proposes the need to integrate the smart city initiative with all the current and future developmental projects. 2023, World Research Association. All rights reserved. -
Mitigation of harmonics for five level multilevel inverter with fuzzy logic controller
Introduction. The advantages of a high-power quality waveform and a high voltage capability of multilevel inverters have made them increasingly popular in recent years. These inverters reduce harmonic distortion and improve the voltage output. Realistically speaking, as the number of voltage levels increases, so does the quality of the multilevel output-voltage waveform. When it comes to industrial power converters, these inverters are by far the most critical. Novelty. Multilevel cascade inverters can be used to convert multiple direct current sources into one direct current. These inverters have been getting a lot of attention recently for high-power applications. A cascade H-bridge multilevel inverter controller is proposed in this paper. A change in the pulse width of selective pulse width modulation modulates the output of the multilevel cascade inverter. Purpose. The total harmonic distortion can be reduced by using filters on controllers like PI and fuzzy logic controllers. Methods. The proposed topology is implemented with MATLAB/Simulink, using gating pulses and pulse width modulation methodology and fuzzy logic controllers. Moreover, the proposed model also has been validated and compared to the hardware system. Results. Total harmonic distortion, number of power switches, output voltage and number of DC sources are analyzed with conventional topologies. Practical value. The proposed topology has been very supportive for implementing photovoltaic based multilevel inverter, which is connected to large demand in grid and industry. M.S. Sujatha, S. Sreelakshmi, E. Parimalasundar, K. Suresh. -
Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM
Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models. 2023 IEEE.