International Journal on Recent and Innovation Trends in Computing and Communication
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IoT-Based Smart Poultry Farming: Enhancing Security and Monitoring for High-Quality Production
Get PDFPoultry producers are having to respond to profound changes on the global farm, a sector that plays a key role in global food security. This research presents a smart poultry farming system using IoT to improve production by ensuring better safety and environmental monitoring. The system utilizes wireless temperature control devices, networked sensors, and associated response mechanisms to regulate temperature, humidity, and ammonia levels, meeting the needs of poultry health and welfare. The system automatically activates exhaust fans based on temperature thresholds (above 35°C and below 20°C) and alerts the owner when ammonia levels exceed 7 ppm. Real-time alerts and on-site analysis are deployed to preempt security risks such as theft and environmental hazards. This comprehensive approach, utilizing real-time data for informed decision-making and exploring future integration of AI for disease detection, highlights significant safety, efficiency, and overall productivity improvements on poultry farms enabled by IoT technology and exemplifies an encouraging direction in which the poultry farm industry is quickly moving
Adaptive Handcrafted Features Convolutional Neural Network for Lung Cancer Detection Using CT Images
Get PDFIn this study, an Adaptive Handcrafted Features Convolutional Neural Network (AHFCNN) for lung cancer detection from CT images is developed. The database is first built using data from web resources. After that, a pre-processing step is created to purge the photos of undesirable information. The pre-processing procedure is also taken into account while enhancing the photos. Because they work on various images with excellence and simplicity, the pixel intensity assessment and histogram techniques are used to improve the image quality. The Grey level cooccurrence matrix (GLCM) and local binary pattern (LBP) are then used to extract the necessary features. Finally, the CT image of the lung cancer is classified using the retrieved features. Convolutional neural networks (CNN) and hybrid meta-heuristic approaches (HMHA) are combined in the suggested classifier. The HMHA was applied to the CNN to select the best gain values. The Coati Optimisation Algorithm (COA) and Honey Badger Optimisation (HBO) are combined to create the HMHA. The HBO was used in the COA to improve the coatis' updating procedure. The proposed methodology was put into practise in Python, and its effectiveness was assessed by taking into account performance indicators including sensitivity, specificity, recall, sensitivity, and F-Score. Recurrent Neural Network- Whale Optimisation Algorithm (RNN-WOA), Deep Belief Neural Network- Remora Optimisation Algorithm (DBNN-ROA), and CNN- Grey Wolf Optimisation (CNN-GWO) are traditional methodologies that are compared to the proposed methodology
Smart Garbage Bin Monitoring and Alert System Framework for Tourist Spots in Daet, Camarines Norte
Get PDFTraditional waste collection methods in tourist hotspots struggle to adapt to dynamic fluctuations in tourist activity. This research proposes a smart waste management framework that leverages Internet of Things (IoT) technology to optimize waste collection and promote responsible waste disposal practices. Smart bins equipped with various sensors form the backbone of the system. Ultrasonic sensors continuously monitor fill levels, while GPS modules provide precise location data. A communication protocol, like MQTT, facilitates efficient and reliable data transmission to a central cloud platform. The cloud platform securely stores all collected data, including sensor readings, timestamps, and GPS coordinates. Data analytics tools are then applied to uncover patterns and trends in waste generation. Time-series analysis allows for a temporal understanding of waste level fluctuations. This can reveal peak generation times associated with tourist activity surges or events. Spatial analysis, visualized through user-friendly dashboards, helps identify areas with consistently high or low waste generation patterns. This empowers waste management personnel to optimize collection routes, minimizing travel distances and fuel consumption. This research proposes a novel data-driven framework for smart waste management in tourist hotspots. The framework leverages IoT technology and data analytics to optimize waste collection, promote environmental responsibility, and enhance the overall tourist experience. This approach paves the way for a more sustainable future for tourist destinations, ensuring a balance between economic growth and environmental protection
Conversational Commerce Blueprint: Strategy, Architecture, and Implementation for the Modern Digital Marketplace
Get PDFThis research & Implementation scrutinizes the strategy and application of conversational commerce along with special emphasis on variables affecting consumer happiness and transactional success. A mixed-methods approach in the study was carried out to find out the effectiveness metrics of user experience, effectiveness of AI tools, various techniques for personalization, and level of interaction with e-commerce websites. Key findings show that, respectively with 85% and 78% of respondents ranking these features as important, simplicity of use and rapid response times are crucial. Hybrid chatbots, combining AI with human interaction, produce the highest rated customer satisfaction ratings. Moreover, product personalization increases user interaction by leaps and bounds, and the completion rates for transactions are higher with full integration of conversational tools with the current systems of e-commerce. This result does make the case for an overall approach that puts a premium on accessible design, effective AI deployment, and tailored experience to succeed in conversational commerce
Building the Future: Unveiling the AI Agent Stack
Get PDFThe envisioned AI agent stack represents a transformative approach to building and deploying artificial intelligence systems, integrating critical components such as vertical agents, hosting and serving infrastructure, observability, agent frameworks, memory, tool libraries, sandboxes, model serving, and storage. This comprehensive architecture aims to enhance the efficiency, scalability, and functionality of AI agents in diverse applications and industries. Vertical agents provide specialized expertise, whereas robust hosting and observability ensure reliable performance and proactive management. Agent frameworks and tool libraries streamlined development, memory components enhanced decision-making continuity, and sandboxes enabled safe experimentation. Model serving and secure storage further supports the deployment and maintenance of advanced AI models. This study explores each component's role, benefits, and challenges, presenting a holistic view of the AI agent stack’s potential to drive innovation and efficiency in AI-driven solutions
Classification of Classical Indian Music Tabla Taals using Deep Learning
Get PDFIn 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
Transforming Education: Understanding How Social Innovation Makes a Difference in Education Sector
Get PDFIn 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
A Novel Method for Self-Driving Solar-Powered Drones
Get PDFThis project presented the transformative potential of integrating solar panels into drones. This innovative approach addresses the long-standing issue of limited battery life, enabling drones to operate continuously, adapt to changing mission demands, and contribute to sustainability efforts in the field of unmanned aerial vehicles. This development represents a significant step forward in the evolution of drone technology, promising a more versatile and self-sustaining future for drones across various sectors. Factors like high-speed flight, aggressive maneuvers, heavy payloads, and adverse weather can dramatically reduce battery life. Surveillance drones, for instance, are confined to covering limited areas before returning for battery changes or recharging. A groundbreaking solution lies in the incorporation of solar panels directly into the drones, allowing them to self-charge when required. This innovation ensures uninterrupted drone operation, regardless of the prospects of energy demands, thus marking a significant step forward in drone technology. With this integration of solar power, drones are poised to become not only versatile but also autonomous, promising a transformative development in the world of unmanned aerial vehicles
Innovative Logistics: Assessing AI’s Impact on Supply Chain Excellence
Get PDFMany facets of corporate operations could be revolutionized by artificial intelligence (AI). Artificial Intelligence (AI) has the potential to improve supply chain inefficiencies, optimize logistics and transportation routes, and forecast demand based on data analysis. This may result in shorter lead times, lower costs, and better response to variations in demand. Using the Scopus database, this study examines and evaluates the uses of artificial intelligence (AI) in supply chain management (SCM).
The goal is to close the existing research gap on the effects of AI on supply chain management (SCM) performance. This includes identifying AI techniques that can improve SCM performance, SCM subfields that have a high potential for AI enhancement, the effects of AI application on SCM performance, and how the performance can be explained from an agile-lean perspective. The Scopus database was used to list and categorize the current nations and areas involved in AI impact on SCM performance, document type, and subject area.
In addition to addressing the existing research gap, this study delves into the challenges and ethical considerations surrounding the integration of artificial intelligence (AI) in supply chain management (SCM). The lack of standardized application methods poses a challenge for cross-enterprise comparisons, making it crucial to explore the variations in outcomes and impacts across diverse industries. Furthermore, the study highlights the absence of standardized metrics for assessing the return on investment (ROI) of AI in SCM, hindering businesses in evaluating the true value of their AI investments.
An essential aspect examined in this research is the integration of AI systems with existing SCM frameworks, revealing potential limitations on data availability and accuracy. Ethical concerns, including issues of discrimination and the protection of sensitive data, emerge as critical considerations that demand greater attention in the context of AI-driven SCM solutions. This study aims to shed light on these ethical dimensions and emphasizes the necessity for a human-centric approach in developing AI solutions, with a focus on workforce development and training alongside process optimization and cost savings
Innovation on Islamic Microfinance Waqf Management: Problems and Strategies
Get PDFBaitul Maal wa Tamwil (BMT) as Islamic microfinance has various business and social functions. This study aims to analyze the problems and its strategies of waqf management in Islamic microfinance institutions. This study employed the Analytic Network Process (ANP) method to discover the priority of problems and feasible strategies. Data were collected from in-depth interviews with experts like academics and practitioners. The finding of this study shows that human resources take priority over other problems with waqf management in BMT. The next top priorities are regulation, management, and literacy. The most feasible strategy is giving regular training to create qualified human resources (nazir). This study contributes to developing the social functions of BMT and so optimizing BMT in their effort to collect low-cost third-party funds as well as supporting BMT continuity