International Journal on Recent and Innovation Trends in Computing and Communication
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    Classification of Classical Indian Music Tabla Taals using Deep Learning

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    In the research that we are bringing to light, we profoundly explore the categorization of Classical Indian Music Tabla Taals. This emphasizes widely recognized taals such as Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. To push the boundaries of our understanding, we implement a mixed-methods approach tethering both Feedforward Neural Networks (FNN) and Convolutional Neural Networks (CNN). These state-of-the-art technologies enable us to dissect and categorize tabla taals efficiently. In essence, the hallmark of Classical Indian music is its complex and multifaceted rhythms brought to life by the primal percussive instrument - the tabla. The conception and reproduction of these nuanced taals require technical finesse. Thus, accompanying the digital revolution and the eclectic musical preferences, it becomes essential for advanced methodologies to pinpoint and classify tabla taals. The hardcover of our research opens up to the magnificent crafting of an unmatched model employing both FNN and CNN. This blend enables us to recognize diverse features unique to tabla taals like Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. The model obtained its bosom knowledge during training from an assortment of Classical Indian music recordings showcasing these invigorating taals. This fosters a broader understanding regarding the array of minute differences brimming within each rhythmic inheritance. To bring user interaction to life, we have embedded a Graphical User Interface (GUI). This empowers users to introduce an audio file filled with table music from the taals listed and receive on-the-spot recognition. refining their connection and knowledge of the taal in question. Our research findings procure paramount importance in the scape of music analysis, especially framed within the heart of Classical Indian Music. We propose a system that would serve as a tool for amateur table players to learn the skill well and master their art. Instructors could also utilize it for training purposes. It opens a new window of possibilities providing an advanced model for intuitive, swift, and accurate automated identification of tabla taals

    Electronic Health Record System using Blockchain Technology

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    The healthcare sector is frequently known for being delicate and intricate.Individuals' sensitive information must be kept safe, secure, and protected.Blocks of the blockchain are secured and bound to each other using cryptographic principles. By maintaining the patient at the centre of the medical ecosystem system and establishing greater security, interoperability, and privacy of stored patient records, blockchain has the potential to eradicate the problems ailing the industry and transform healthcare. By decentralizing and encrypting health records, blockchain ensures that patient data is securely stored and tamper-proof. Additionally, blockchain can facilitate the seamless exchange of medical information between different healthcare providers, leading to better coordination of care and reduced medical errors. By leveraging Ethereum's smart contract functionality, healthcare organizations can securely store and share patient data, ensuring its integrity and confidentiality. Moreover, Ethereum's programmable nature allows for the development of decentralized applications (DApps) that can streamline various healthcare processes, such as medical record management, supply chain tracking, and clinical trials. Overall, the integration of blockchain in the healthcare industry has the potential to revolutionize the way healthcare data is managed, ensuring privacy, security, and efficiency in patient care

    Machine Learning Algorithms in Cloud Manufacturing - A Review

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    Cloud computing has advanced significantly in terms of storage, QoS, online service availability, and integration with conventional business models and procedures. The traditional manufacturing firm becomes Cloud Manufacturing when Cloud Services are integrated into the present production process. The capabilities of Cloud Manufacturing are enhanced by Machine Learning. A lot of machine learning algorithms provide the user with the desired outcomes. The main objectives are to learn more about the architecture and analysis of Cloud Manufacturing frameworks and the role that machine learning algorithms play in cloud computing in general and Cloud Manufacturing specifically. Machine learning techniques like SVM, Genetic Algorithm, Ant Colony Optimisation techniques, and variants are employed in a cloud environment

    ?Implementation of Security Protocol for Intrusion Detection Systems in Wireless Sensor Networks

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    Sensor networks consist of compact sensors and actuators capable of monitoring physical conditions. Wireless Sensor Networks (WSNs) with limited power and dynamic topology require effective security mechanisms. Insider attacks pose a greater challenge than outsider attacks. This work proposes an Intrusion Detection approach in WSNs to detect attacks, emphasizing experimental results, parameter analysis, and Performance Evaluation based on accuracy and minimizing false positives

    Batteries on the Move: Navigating Challenges, Expanding Horizons for Indian EVs

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    In India, the widespread adoption of swappable battery systems for electric vehicles faces significant hurdles such as regulatory gaps, infrastructure limitations, technological constraints, economic uncertainties, and environmental complexities. Overcoming these challenges demands a cohesive strategy involving thorough regulatory assessment, infrastructure expansion, tech collaboration, financial scrutiny, and environmental evaluations. This holistic approach is key to unlocking the untapped potential of swappable batteries, paving the way for an innovative, eco-friendly electric mobility landscape in India. This research paper aims to provide valuable insights for policymakers, industry players, and researchers grappling with the adoption of swappable battery systems in India's EV sector

    Transforming Education: Understanding How Social Innovation Makes a Difference in Education Sector

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    In the face of persistent educational challenges and the evolving demands of the 21st century, social innovation has emerged as a beacon of hope, offering a transformative approach to reimagine and reshape education systems. This paper delves into the concept of social innovation in the education sector, elucidating its defining characteristics, examining its profound benefits, and acknowledging the challenges it presents. Through a comprehensive review of existing literature, the paper showcases compelling case studies and examples of successful social innovations that have revolutionized educational landscapes. Education, the cornerstone of societal progress, faces persistent challenges in the 21st century. Social innovation emerges as a beacon of hope, offering a transformative approach to reshape education systems. Social innovation in education aims to address social needs, fosters collaborative partnerships, and prioritizes sustainability. It seeks to dismantle educational inequities and empower all learners to reach their full potential. The benefits of social innovation include enhanced learning outcomes, championed equity and inclusion, and preparation for the future. However, sustainability, scalability, measurement, and collaboration pose significant challenges. A growing body of research showcases successful social innovations that have revolutionized educational landscapes. Embracing social innovation can pave the way for a more equitable, effective, and future-ready education system

    Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning

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    Ransomware attack incidences have been on the rise for a few years. The attacks have evolved over the years. The severity of these attacks has only increased in the cloud era. This article discusses the evolution of ransomware attacks targeting cloud storage and explores existing ransomware detection solutions. It also presents a methodology for generating a dataset for detecting ransomware in the cloud and discusses the results, including feature selection and normalization. The article proposes a system for detecting attacks in virtualized environments using machine learning models and evaluates the performance of different classification models. The proposed system is shown to have high accuracy of 96.6% in detecting ransomware attacks in virtualized environments at the hypervisor level

    Analysis of Vision based Techniques for the Translation of Indian Sign Language

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    Sign language acts as a medium of communication among those of the hearing impaired and mute community. However, it cannot be easily understood by common people. Various research has been done to bridge this gap by developing Sign Language Recognition (SLR) methodologies. Studies say that 1 in every 5 deaf people is Indian. In this paper, a thorough review of these methodologies has been done, to compare and contrast various aspects of them. This includes an overview on different preprocessing methods used like segmentation, image morphological processing, cropping, etc, feature extraction techniques like Fourier Descriptors, Image Moments, Eigen values, Mediapipe and others. This study also covered classification models spanning from Distance metrics to Kernel based approaches and feedforward neural networks, along with Deep Learning based methods such as CNNs, LSTMs, GANs, Transformers etc

    A Study on Financial Performance of Selected Public Sector Banks

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    Public sector banks are those in which the government holds more than 50% of the total stock. The government formulates all the financial guidelines for public sector banks. The public sector banks operate under the government to inspire trust in the depositors that their money is safe. The aim of the study is to find out the financial performance of selected Public Sector Banks using ratio analysis. Tools used for the study Mean, F-test or ANOVA (Analysis of Variances). Conclusion of the study Public sector banks want to increase the management capability to increase the profits,ROE, EPS, ROI and to increase the efficiency of banks

    Zero-day Network Intrusion Detection using Machine Learning Approach

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    Zero-day network attacks are a growing global cybersecurity concern. Hackers exploit vulnerabilities in network systems, making network traffic analysis crucial in detecting and mitigating unauthorized attacks. However, inadequate and ineffective network traffic analysis can lead to prolonged network compromises. To address this, machine learning-based zero-day network intrusion detection systems (ZDNIDS) rely on monitoring and collecting relevant information from network traffic data. The selection of pertinent features is essential for optimal ZDNIDS performance given the voluminous nature of network traffic data, characterized by attributes. Unfortunately, current machine learning models utilized in this field exhibit inefficiency in detecting zero-day network attacks, resulting in a high false alarm rate and overall performance degradation. To overcome these limitations, this paper introduces a novel approach combining the anomaly-based extended isolation forest algorithm with the BAT algorithm and Nevergrad. Furthermore, the proposed model was evaluated using 5G network traffic, showcasing its effectiveness in efficiently detecting both known and unknown attacks, thereby reducing false alarms when compared to existing systems. This advancement contributes to improved internet security


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    International Journal on Recent and Innovation Trends in Computing and Communication
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