Volume 14 | Issue 2

 Abstract

Water is a critical resource, and its sustainable management is essential to meet the growing demands of agriculture while preserving the environment. This study explores the integration of smart water metering, autonomous irrigation, renewable energy, and big data analytics to enhance agricultural productivity and conserve resources. A cloud-based Internet of Things (IoT) framework enables real-time monitoring, recording, and analysis of water consumption, water table levels, temperature, humidity, soil moisture, and light sensor data. Big Data Analytics to harness real-time data from sensors monitoring temperature, humidity, soil moisture, and light intensity. By processing and analysing vast datasets, the system derives actionable insights to optimize irrigation schedules, energy consumption, and crop management strategies. The Big Data platform enables predictive modelling and trend analysis, improving longterm planning and resource allocation. Smart water metering ensures precise and efficient water distribution, delivering water only where and when needed. Renewable energy sources, such as solar and wind power, reduce dependence on fossil fuels, making agriculture more energy-efficient and eco-friendly. Autonomous irrigation systems, powered by real-time data, enhance crop quality, productivity, and soil health while mitigating issues such as waterlogging and over-irrigation. Additionally, RFID technology enables seamless replication of the system across lands with similar crop and soil conditions, ensuring scalability and operational consistency. This comprehensive solution safeguards aquifers, maintains the water table, and addresses critical environmental challenges. By integrating these advanced technologies, the study provides a sustainable, scalable framework to balance agricultural productivity with environmental conservation, paving the way for resilient farming systems.

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Smart Water Metering; Renewable Energy Integration; Water-table Conservation; Energy efficient Agriculture; Soil Health;

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In this paper, a new virtual piano system is proposed, with an emphasis on the use of a monocular camera system for gesture-based musical input with no dependence on keys. The system utilizes state-of-art hand and fingertip tracking built upon the principles of computer vision. One enhancement, in particular, is the velocity-sensitive key press detection mechanism used to detect rapid downward finger movement as the musical note triggers, much like the action of a piano. The concepts of dynamic calibration applied to an idealized reference line representing the edge of the desk and the study of accidental key presses resulting from hand movements not involved in keying enhance accuracy by clearly defining an exclusion zone that must not be crossed and minimizing interference from false contact with the keyboard. The system incorporates dynamic calibration so as to account for differing heights of the desk together with camera direction to make certain that it performs optimally in several situations. Efficiencies in tracking algorithms and feedback systems reduce latency delivering a dynamic and engaging application. Additional features include real-time fingertip highlighting and note names to facilitate user participation and to give feedback support. The proposed system shows that monocular camera-based solutions have the ability to provide an efficient way of constructing portable and accessible virtual musical instruments. The mentioned concepts include its use in music learning and teaching, as gesture-controlled devices, and augmented- or virtual-reality-based music applications. This paper discusses features of gesture recognition, interactive music systems, and computer vision with a focus on building a new approach to virtual instrument control.

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Monocular Camera, Hand Gesture Tracking, Computer Vision, Interactive Music Systems.

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Agriculture and mathematics are very much related to each other. A linear programming technique from Operations Research, which is a branch of mathematics, is used to optimize the crop yield or crop production.This research paper throws light on how to optimize crop yield by the simplex method by using Excel.

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Agriculture, mathematics, crop yield

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In today's era of big data, machine learning is the modern technology. Machine learning is nothing but the application of algorithms to solve the real-life problems. Mathematics provides useful tools for data representation, matrix multiplication, optimization, and decision-making in machine learning algorithms. This paper highlights the importance of these mathematical tools, which include linear algebra, calculus, and Probability theory. The important pillar of a machine learning algorithm is data. Linear algebra is useful for representing data in matrix form systematically and for reducing the dimension of the given data, making it easier to handle large datasets. Since the data is in matrix form, various operations can be performed using matrix operations. (Linear Transformations). Moreover, in the optimization part of a machine learning algorithm, calculus plays a crucial role. Probability theory is very much useful in the decision-making step of machine learning algorithms. So, mathematics acts as a building block for modern technology, machine learning. With the help of Mathematics, we can better understand the working behavior of Machine Learning. Knowledge of Mathematics helps us choose the appropriate or correct algorithm for a given data set and also to improve the accuracy of the machine learning algorithms.

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Machine learning; Linear Algebra; Calculus; Probability Theory

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Children's cognitive development is essential in the future of a nation. Strong, solid, well-educated and well equipped children are crucial for a country. Memory, attention, perception and logical problem solving are parts of the development of the cognitive process. The objective of this research study is to explore the entertainment factors and family perception that influencing children's cognitive development. This research will provide parents with much needed and valuable insight into the ways to better protect future generations. The proposed study adopts a quantitative research strategy and obtains data through structured questionnaires. Children are assessed in terms of perception, attention, problems solving, and memory. Data analysis reveals variation in mental skills of children and possible gaps that demand more support and interventions. Results show that parental occupation, screen exposure or toy play justification may have a negative effect on a child's concentration ability and reasoning ability decline. This study also finds gender differences in cognitive skills evaluations.

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Cognitive development, entertainment, family perception, children, and cognitive skills

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Marathi is one of the popular oldest languages of India and possesses the greater syntactic complexity. Nouns, verbs and compound words of this language have very clear and simple rules that make the learning of the language an easy task for anybody. However, a more effective tool is required to consolidate the particularity of the higher level grammar, especially the regional dialects. In the last few years there has been a lot of work done in the era of Artificial Intelligence (AI) and Natural Language Processing (NLP) where various languages related complicated tasks have been made easy. Pre-trained generative AI models like BERT (Bidirectional Encoder Representations from Transformers), GPT-3 (Generative Pre-trained Transformer), T5 (Text-to-Text Transfer Transformer) and many others hold much promise in different language uses like text generation, translation, and grammar checks. These models are capable of formulating, interpreting and modifying any linguistic behaviour that might be useful in handling other challenges of Marathi language such as noun declensions and verb conjugations. AI model built from the transformer architecture can be adapted to handle several of linguistic problems in Marathi language that involves syntax analysis and error detection. This paper presents a generative AI model for learning the Marathi grammar which is further categorized into two parts. The first part of the study is devoted to the elementary grammar training and the second part is dedicated to the intermediate and advanced levels. Through the use of generative AI, this current model gives higher accuracy than rule-based system and presents effective ideas towards modern grammar. Also this model became improving tools in computational linguistics for regional languages and for promoting language education.

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Marathi language, generative AI, BERT, GPT-3, T5, computational linguisticsii

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This research paper demonstrates the implementation of advanced machine learning filters used for collaborative and content-based recommendations. The delivery of personalized recommendations depends on these approaches because they analyze patterns of user choices together with elements of movies. The exploration builds recommendation accuracy through a K-Nearest Neighbors (KNN) and Recurrent Neural Networks (RNN) amalgamation. The joint operation of KNN and RNN services achieves optimal performance through rapid item and user similarity evaluation from KNN and RNN's sequential data processing for tracking user taste changes. Various filtering approaches that merge collaborative and content-based methods receive evaluation in this research through accuracy assessments and recommendation expansion evaluations. The collaborative filtering framework helps content-based filtering systems accomplish their collection process by performing item-item comparison operations. Initial recommendations for matching movies originate from built-in content features which include genre classifications and directorial credits combined with casting information. Hybrid recommendation models combine different recommendation methods in order to address collaborative systems' cold-start problems and content-based approaches' targeted application conditions. For this research, the datasets used are the Netflix Prize dataset and MovieLens, which are famous for their huge and diverse movie data. The datasets offered in these provide a solid basis for training and testing the proposed model. The study also compares the coverage and data quality with the IMDb Top 1000 dataset. Therefore, the Netflix Prize dataset provides large user-movie interaction data; MovieLens provides detailed movie metadata and user ratings, thereby achieving a fair evaluation of the system. The proposed hybrid approach produces various advantages to enhance user-specific recommendations and dynamic response capabilities as well as better prediction accuracy. The research notes that RNN component training requires extensive datasets while also accepting the implementation challenges of this hybrid system. Although challenging to implement the hybrid system demonstrates promising capabilities to deliver relevant movie suggestions at the appropriate times. The recommendation system which combines KNN and RNN enables development in research for movie recommendation platforms. The suggestion generation process in the system uses algorithm-matched technology to link content-based approaches with collaborative filtering for individualized recommendations. The core principles described in this work can help industrial fields strengthen their hybrid recommendation system creation process.

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Movie recommendation, collaborative filtering, content-based filtering, RNN, KNN, Netflix Prize, MovieLens, IMDb.

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The distinctive linear and nonlinear optical characteristics of chalcogenide thin films, particularly containing selenium (Se), have aroused considerable attention in photonics research. We examine how proton irradiation changes the optical characteristics of thin films made of chalcogenides. from a theoretical and experimental perspective in this work. Researchers have found that proton irradiation can change the physical and optical properties of these films, which in turn changes how well they work in a number of photonic uses. We look at how proton treatment changes the nonlinear optical behavior by looking at changes in transmission, absorption, and the refractive index. The potential applications in fields like optoelectronics and photonics are examined.

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Chalcogenide Thin Films, Proton Irradiation, Linear optical Properties, Nonlinear Optical Properties, Photonic Applications

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Anemia is a very common medical condition in which the count or size of red blood cells is reduced, and it limits oxygen transport to the body. In most cases, it requires invasive and costly blood tests for diagnosis. India is Struggling to keep up the World Health Assembly targets for anemia reduction by 2025 .Anemia Mukt Bharat strategy Introduced in 2018. It is Aiming at iron and folic acid Nutritional support to adolescent girls as they provide the Renewed opportunity to eliminate or reduce the burden and break the intergenerational cycle of anemia before entering into the pregnancy [16]. Strengthening adolescents' nutrition is beneficial to adult health. It produce triple dividends- better health for adolescents now, Enhanced health and performance in their future adult life and lowered health risks for their offspring. (World Health Organization, 2018) At this stage, there is still the chances of correcting nutritional deficiencies and possibly even bridging the gap on growth. Nutrition Interference in adolescent may help break the cycle of malnutrition, chronic disease and poverty. Adolescent girls' health and their status in general, and the frequency of anaemia in particular, are affected by factors such as deworming, underweight, vegetarianism, obesity and the presence of pallor. Some other factor have been found to be associated with anemia such as socioeconomic status, education, worm infestation, menstruation, and pregnancy in adolescent females [1] This research addresses the gap between traditional diagnostics in health and modern non-invasive approaches by using image analysis and mathematical modelling [11] through matlab, which would provide a scalable solution for screening Anemia in resource-constrained areas.

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Anemia Detection; Non-invasive Diagnosis; Image Processing; Curve Fitting; Hemoglobin Prediction

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This article aims to study the time-space fractional biological population model which describe population densities in the various biological movements. To find an approximate solution for the time-space fractional population model, we employ the Homotopy Perturbation Method (homotopy perturbation method), a powerful analytical technique for solving nonlinear fractional differential equations. Also, we prove the convergence of the developed method. Fractional-order one-dimensional biological model for the spread of genes in a population and a twodimensional biological population model with Verhulst law are studied. Traveling wave solutions are observed for both one and two-dimensional models. The obtained results confirms that the proposed time-space fractional model provides valuable dynamic behavior of biological populations in fractional environments. Analytical and numerical solutions of models are presented in the form of tables and graphs with the help of SageMath programming..

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Fractional order biological population model, Homotopy perturbation method, Convergence, Fractional Calculus, SageMath, etc.

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Parents prioritize the well-being of their infants, and technological advancements have enhanced safety, comfort, and convenience for both babies and caregivers. This paper presents the development and assessment of a smart cradle designed to monitor and respond to a baby's needs through built-in sensors. These sensors collect real-time data accessible via a mobile app, allowing for continuous monitoring and timely interventions. The cradle automatically adjusts to ensure the baby's comfort and hygiene and features a hands-free mode that dynamically positions it relative to the parent. Preliminary results from testing the prototype indicate that this smart cradle significantly enhances caregiving by providing essential support and improving the overall parenting experience. By leveraging innovations in childcare technology, this study highlights the potential of smart cradles to transform traditional caregiving practices.

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Smart Cradle, Real-Time Monitoring, IoT, Autonomous Features, Modern Parenting Solutions, Hands Free Mode

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Bone fractures tend to be one of the commonest medical conditions that need prompt and accurate diagnosis for the right treatment and healing. The traditional methods for detecting fracture rely a lot on human errors and long manual analyses of X-ray images. Bone fracture detection forms a lot of essential diagnoses in this medical field, taking long time durations. This research proposes a machine learning system based on a deep CNN model to achieve automatic bone fracture identification. This convolutional neural network model will be trained on a large dataset of X-ray images to learn the patterns associated with the fracture. Training, testing, and validation comprise the three sections of the dataset. The efficiency of the suggested approach in detecting with high perfection is demonstrated by its 96.33% accuracy rate. When compared with traditional methods, the use of CNNs significantly decreases the amount of time needed for diagnosis and increases the overall accuracy of fracture identification. The system can precisely detect bone fractures thanks to the suggested model's influence on a deep CNN architecture that extracts characteristics from X-ray pictures. A widely utilized technique in image processing and computer vision, canny edge detection is frequently used in combination with CNNs to detect bone fractures. The dataset consists of medical photos with annotations that have been pre-processed for augmentation and normalization to increase the robustness of the model. By getting the AI based solutions integrated into clinical workflows, this research signifies the great role deep learning can play in the revolution of fracture diagnosis.

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CNN; X-ray images; Bone fracture; Deep Learning; Medical Image Analysis.

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Imagine, if everything that you're looking for is already within earshot, yet one can never even get a look at it. The Real-Time Narration Tool (RTNT) is the first in the world to convert visual information directly into real time audio narration. Be it your basketball game at the tail end being described as a buzzer beater, or just how pretty is that sunset out there, or be you in the middle of a car chase with someone when live video streaming comes into effect; RTNT affords humankind access to information they hitherto lacked via their ear drums. Such an earth shaking feat is existing via harnessing services across any technological trifecta. RTNT runs on portions of computer vision, natural language processing, and text-to-speech generation. It first admits that there is a live video feed and within milliseconds determines scenes such as GPT technology and responds with guided narratives to action and intention. With Eleven Labs text-to-speech, the oral output is so natural that one cannot tell if a computer is speaking or a human is rendering a story. Besides just testing this tool on many successful challenges, I tested it in practical situations, worldwide. It worked wonders--with implications for amazing realities--in arenas and auditoriums, gameplay, and classrooms. It has an innate sense of purpose for functionality where interfacing with arenas that are usually only seen is made accessible to many different types of doers. Even interfacing mixed realities such as virtual realities opens up blended proceedings of access and actionable response. The possibilities are endless. Future versions include multilingual offerings for global audiences and even more AI-smart narratives that learn more than merely responding to directives. This RTNT will one day service the universe and the realms of accessibility, education, and entertainment--not merely as another narrative experience that so many take for granted--but as something that will change how people experience life in their worlds with no one left behind

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Real-Time Narration; Accessibility; Natural Language Processing; Audio Descriptions; Voice Synthesis

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Quantum cryptography is revolutionizing secure communication systems by providing unprecedented security advancements, particularly through Quantum Key Distribution (QKD). The invention of the QKD system has significantly increased the level of security in exchanging private keys. This paper focuses on the study of three widely recognized QKD protocols--BB84, SARG04 and E91. We used IBM's quantum computing framework, Qiskit, to simulate these protocols. We assessed and compared these algorithms by simulating them on our local machine via Qiskit's "qasm_simulator." Our focus was on key generation rates and error rates both in the presence and absence of an eavesdropper. We experimented using varied bit lengths (i.e., number of qubits) to observe how the protocols behave across different scales. Additionally, this study incorporated noise models such as FakeRochester, FakeMelbourne and FakeParis to simulate real-world imperfections such as decoherence and evaluate the simulator's ability to accurately model noise. The results revealed that SARG04 consistently achieved perfect key generation, whereas BB84 demonstrated greater resilience, particularly with longer bit lengths, compared to E91, which performed poorly in noisy environments. The selection of these protocols might be contingent upon particular application needs and their resistance to noise and eavesdropping. Future work could explore enhancements to these protocols or hybrid approaches that combine their strengths to achieve even greater efficiency and security in quantum cryptographic systems.

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Quantum Key Distribution; Quantum Cryptography; BB84; E91; SARG04

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In this digital era, social media is a popular & powerful tool to communicate digitally with each other. This daily communication generates the massive amount of electronic data on web. Processing this huge data is a challenging task. Hence social media data processing is gaining more focus. Hate speech detection is one of the important parts of social media data processing. This paper presents the review of hate speech detection systems developed using machine learning techniques for Indian and Non Indian Languages.

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Hate Speech, Social Media, Machine Learning Algorithms, Indian and Foreign Languages

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This paper aims at constructing a user defined functions in Macaulay2 which accepts values of ?n ? and ?q ?with gcd(n, q) = 1 and displays all q - cyclotomic cosets modulo n . This list of cyclotomic cosets can help in describinga cyclic code in Macaulay2 by selecting a representative from each cyclotomic coset in its defining set. For a given cyclic code ?C ? and a positive integer?w?, there is a well-known algebraic system constructed from Newton's identities which are satisfied by elementary symmetric functions of locators of a codeword of weight ?w? and coefficients of its Mattson-Solomon polynomial. This paper aims at constructing a user defined function in Macaulay2, which accepts a cyclic code (in terms of list of elements from distinct cyclotomic cosets in its defining set) and a positive integer ?w? and it returns the algebraic system described above. Further, the simplified form of this system is also constructed.In a special case when integer w is equal to BCH bound of C, the simplified system is used for computing number of codewords of minimum weight in C, using Gro?bner basis.

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linear codes, Cyclic code, BCH code, Mattson-Solomon polynomial, locators of a codeword

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This study delves into the application of multispectral imagery for forest fire monitoring, aiming to enhance early detection, assessment, and management strategies through remote sensing techniques. Key components include an exploration of multispectral image properties, an introduction to Landsat-8 satellite technology, spectral bands analysis, and various applications of multispectral imagery, particularly in forestry. The study examines species identification, deforestation monitoring, and fire detection and management, employing methods such as polygon area index extraction and pattern analysis. Notably, the utilization of vegetation indices like NDVI, NBR, and dNBR enables effective prediction and detection of forest fires. Additionally, through a case study in Simlipal, Orissa, implementation using QGIS software demonstrates practical application and validation of methodologies. The study contributes novel insights into forest fire assessment, highlighting the significance of multispectral imagery in understanding fire impacts. By analyzing average NBR values, the study discerns areas affected by forest fires, providing critical data for post-fire assessment and mitigation efforts. Overall, this research advances forest fire monitoring capabilities, aiding in ecosystem preservation and safeguarding lives and property

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Multispectral Imagery, Forest Fire Monitoring, Remote Sensing, Landsat-8, Vegetation Indices

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Melanoma is a very dangerous kind of skin cancer for which early diagnosis is crucial for successful treatment.Inmost cases,deep learning models such as convolutional neural networks (CNNs) can be employed to classify skin lesions accurately. But becausethey are black-box systems, they cannot be implemented in hospitals since they are not transparent.This paper presents asystemthatintegrates an advanced CNN algorithm named EfficientNet with Explainable AI (XAI) methods to improve accuracy, interpretabilityand transparency in melanoma detection. The model is trained on the ISIC 2016 part 3 dataset, which consist of heterogeneousdermoscopic images of benign and malignant skin lesions. XAI methods offer text explanations of predictions,by indicatingthemostimportant features like asymmetry or border irregularities with audio explanations.The SHAP and LIME are utilized todemarcatetheregions impacted and the contribution of every attributes towards the malignant character. This enhances the process of explanationand informs decision-making. A web application accessible via a user-friendly interface is created using Gradio that allowspatientsand clinicians to upload lesion images for real-time analysis. The web application produces reports, such as predictions,confidencescores and rationales, which are downloadable for use in clinical settings.Comparison is made with a traditional CNNandEfficientNetB3 in accuracy, efficiency, and generalization. Experimental results indicate that EfficientNetB3 has anaccuracyof92.7%, surpassing CNN (84.5%) while retaining computational efficiency. This system solves critical challenges inmelanomadiagnosis by enhancing accuracy and induces trust through interpretability. The principal aim of this project is tominimizeunnecessary biopsies while detecting melanoma and improve early detection leading to improved patient outcomes.

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Convolutional neural networks(CNNs), EfficientNet, Skin lesion classification, XAI, ISIC 2016 Part 3, Gradio.

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Real-time image processing is usually associated with the problems of system slowness and image quality decline, especially in low-light conditions. This work proposes a novel approach utilizing a three-dimensional channel flexing mechanism for brightness enhancement and noise reduction. The channel flexing mechanism operates along three dimensions under a multistacked structure with the K-means clustering technique to enhance image brightness and minimize noise. The separation of the illuminated and dark images facilitates conversion to target pixels. The presented three-dimensional channel flexing technique employs triggers for dynamically swapping between the Red, Blue, and Green channels to avoid introducing luminous regions into oversaturation. The energy is allocated uniformly among respective clusters for higher PSNR. The standard assessment of the processed images has been done based on three major parameters: PSNR, SSIM, and MAE. The evaluation metrics show that this technique provides good visual quality and computation time, proving its adequacy for real-time image enhancement. The results imply that this strategy is particularly suitable for real-time visual enhancement in computer vision systems.

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Real-time image processing, Image enhancement, Denoising, Low-light conditions, MLS-UNET Model, ThreeDimensional Channel Flexing, K-means clustering, PSNR, SSIM, MAE.

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In today's fiercely competitive and dynamic retail landscape, cultivating extraordinary in-store customer experiences, encouraging loyalty and achieving large market dif erentiation are primary to retail success. An important number of people want more than just products; they want shopping experiences that are personal, ef icient and memorable and that will make their lives better. The of ice experience is central to the decision-making process, because most buyers choose to purchase there. Our smart fitting room system uses barcodes to update the customary fitting room experience.

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Database, Internet of Things, Customer Experience, Barcode Technology, Technology Driven Solution, Modern Environment, Smart Fitting Room.

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