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Intellectual property rights vis-a-vis food security: A critical analysis
The Right to Food is undoubtedly a human right since it is one of the basic necessities without which it is impossible to sustain life. Food Security refers to the availability as well as accessibility to sufficient and quality food by all individuals. However, there persists a problem of food insecurity which is a major problem especially in the underdeveloped and, to a considerable extent, the developing countries. At an individual level, food security is limited to one's access to food but on a broader sense food security cannot be isolated from agricultural policies, crop technologies, economic and trade conditions. The intersection of crop technologies with economic factors is what links food security with Intellectual Property Rights (IPR). Over the recent past, IPR has gained immense importance in a number of fields including agriculture. It provides the incentive for the private sector development in advancement of plant science and crop technologies which helps in ensuring food security in the long term. This study aims at discussing the issues of food security with a specific focus on the developing nations, IPR regime, and its introduction into the agriculture sector. It intends to explore the connections and linkages between IPR and food security, especially how intellectual property can act as a medium to cover the path toward achieving global food security. The author aims to put forth the ability of IPR as a means to achieve food security by incentivising human creativity through a detailed study from an international as well as region-specific perspective. 2023 Apple Academic Press, Inc. All rights reserved. -
Intelligence in Children Whose Either Parent Is Treated For Schizophrenia.
G.J.B.A.H.S.,Vol.2(4):119-123- October- December ISSN: 2319-5584 -
Intelligence-Software Cost Estimation Model for Optimizing Project Management
With the evolution of pervasive and ubiquitous application, the rise of web-based application as well as its components is quite rising as such applications are used both for development and analysis of the web component by developers. The estimation of software cost is controlled by multiple factors right from human-driven to process driven. Most importantly, some of the factors are never even can be guessed. At present, there are no records of literature to offer a robust cost estimation model to address this problem. Therefore, the proposed system introduces an intellectual model of software cost model that is mainly targets to perform optimization of entire cost estimation modeling by incorporating predictive approach. Powered by deep learning approach, the outcome of the proposed model is found to be cost effective in comparison to existing cost estimation modeling. 2019, Springer Nature Switzerland AG. -
Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
Intelligent agriculture - Smart IOT system to assist farmers in effective decision making using data science /
Patent Number: 202141046585, Applicant: Dr.S.Balamurugan.
Research studies shows that the current world population of 7.3 billion is expected to increase to 9.3 billion by the ear 2050. In order to feed the increasing population, Food and Agricultural Organization (FAO), plans to increase the crop cultivation by 70%. Recent days have seen a steep rise in the adoption of IoT to various factors affecting agriculture like climate change monitoring, greenhouse automation, crop cultivation and management, cattle monitoring and management, precision farming, agricultural drones, predictive analysis for smart farming and many more. This invention discloses a Data-driven smart IoT system to help farmers for effective decision making on the choice of the crop to be cultivated in the given time. IoT sensors are capable to predict the humidity in the soil, nature of chemical resources that are apt for cultivation and weather forecasting. -
Intelligent approach to automate a system for simulation of nanomaterials
Nanomaterial composites are generally found to have great thermal properties and hence have witnessed an increasing demand in the recent years for manufacturing of efficient miniature electronic devices. The process of finding the right composites that exhibit the desired properties is a rather tedious task involving a lot of trial and error in the current scenario. This paper proposes a methodology to digitize and automate this entire process by administering certain efficient practices of assessing the properties of nanomaterial like Coarse Grained Molecular Dynamics thus resulting in faster simulations. 2023 Author(s). -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Intelligent Course Recommendation for Higher Education based on Learner Proficiency
A course recommendation provides valuable guidance and support to learners navigating their educational and career journeys. Artificial Intelligence paves the way for recommending higher education courses. In this article, a framework is proposed that uses different features like learners' interest, their past performance and mainly their family talent history. This framework emphasizes the Intelligence Robotic Course Recommendation System. The system is very helpful for the learner who don't have that much of an explorer of the current trends happening in the world. When the learners similarity knowledge interest is known with respect to real-world needs, the perfect higher education is suggested for them. This paper shows that the framework gives better results when using with artificial intelligence algorithms. 2023 IEEE. -
Intelligent deep-well rescue system using ultrasonic sensors /
Patent Number: 201941048191, Applicant: Dr. Debabrata Samanta.
The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well. -
Intelligent deep-well rescue system using ultrasound sensors /
"Patent Number: 201941048191, Applicant: Debabrata Samanta.
The present invention is related to an intelligent deep-well rescue system using ultrasonic sensors. The system for rescue a human in a narrow diameter deep-well (bore-well) comprises an ultrasonic sensor module, a grappling module, a central computing unit. The objective of the present invention is to solve the problems of the prior arts in solving issues of rescue of human being from the deep-well or bore well." -
Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits. 2022 by the authors. -
Intelligent Environmental Data Monitoring for Pollution Management: A volume in Intelligent Data-Centric Systems
Intelligent Environmental Data Monitoring for Pollution Management discusses evolving novel intelligent algorithms and their applications in the area of environmental data-centric systems guided by batch process-oriented data. Thus, the book ushers in a new era as far as environmental pollution management is concerned. It reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, followed by a review of intelligent tools and techniques which can be used in this direction. In addition, it discusses novel intelligent algorithms for effective environmental pollution data management that are on par with standards laid down by the World Health Organization. To learn more about Elseviers Series, Intelligent Data-Centric Systems, please visit this link: https://www.elsevier.com/books-and-journals/book-series/intelligent-data-centric-systems-sensor-collected-intelligence. 2021 Elsevier Inc. All rights reserved. -
Intelligent Information Retrieval Model for Digital Documents in Title Insurance
Documents have been pivotal in shaping human history by preserving knowledge and newlineenabling the transmission of ideas across generations and cultures. They have facilitated the establishment of legal systems, institutions, and governance, fostering societal order and progress. Additionally, documents serve as a collective memory, chronicling the achievements and lessons learned, enriching the human experience. Transforming documents from physical to digital format has revolutionized how we access, store, and share information in the digital age. This transition, enabled by technological advances, began with the invention of the scanner, which allowed for newlinethe digital capture of images and text. Optical Character Recognition (OCR) technology that can convert scanned documents into searchable, editable digital texts further streamlined this process. As the storage capacity and internet speeds have increased, digitization has become more accessible and widespread. Cloud-based storage solutions, such as Google Drive and Dropbox, now allow users to store, access, and share digital documents from anywhere with an internet connection. This has improved collaboration and communication and reduced the need for physical storage space. The digitization of documents has also significantly impacted the environment, with paper consumption decreasing and many industries carbon footprint reducing. Libraries and archives have transformed digitally, making vast information more easily accessible and preserving vital historical records for future generations. This digital shift has democratized knowledge, granting people worldwide access to resources that were once limited newlineto those with physical proximity to the material. -
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Intelligent load shedding using ant colony algorithm in smart grid environment
For every country which is expecting a large growth in power demand in the near future or facing a power crisis, an effective load control and power distribution strategy is a necessity. Load shedding is done whenever power demand is more than power generation in order to sustain power system stability. The current load shedding strategies fails to shed exact amount of load as per the system requirement and does not prioritize loads which are being shed. Given the dimension of the problem, it would not be feasible computationally, to use regular optimization techniques to solve the problem. The problem is typically suited for application of meta-heuristic algorithms. This paper proposes a new scheme for optimizing load shedding using ant colony algorithm in a smart grid platform considering loads at utility level. The algorithm developed considers each electrical connection from Distribution Company as one lumped load and provides an effective methodology to control the load based on various constrains such as importance of load and time of load shedding. Springer India 2015. -
Intelligent machine learning approach for cidscloud intrusion detection system
In this new era of information technology world, security in cloud computing has gained more importance because of the flexible nature of the cloud. In order to maintain security in cloud computing, the importance of developing an eminent intrusion detection system also increased. Researchers have already proposed intrusion detection schemes, but most of the traditional IDS are ineffective in detecting attacks. This can be attained by developing a new ML based algorithm for intrusion detection system for cloud. In the proposed methodology, a CIDS is incorporated that uses only selected features for the identification of the attack. The complex dataset will always make the observations difficult. Feature reduction plays a vital role in CIDS through time consumption. The current literature proposes a novel faster intelligent agent for data selection and feature reduction. The data selection agent selects only the data that promotes the attack. The selected data is passed through a feature reduction technique which reduces the features by deploying SVM and LR algorithms. The reduced features which in turn are subjected to the CIDS system. Thus, the overall time will be reduced to train the model. The performance of the system was evaluated with respect to accuracy and detection rate. Then, some existing IDS is analyzed based on these performance metrics, which in turn helps to predict the expected output. For analysis, UNSW-NB15 dataset is used which contains normal and abnormal data. The present work mainly ensures confidentiality and prevents unauthorized access. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021. -
Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities
The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic. This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing. Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production Discusses the improvement in service quality, product quality, and production effectiveness Conveys how a manufacturing companys competitiveness can increase if it can manage the turbulence and changes in the global market Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
Intelligent Manufacturing Components, Challenges, and Opportunities
Intelligent Manufacturing shows transformative paradigms in the manufacturing industry; leveraging advanced technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and robotics, to develop highly automated and adaptive production systems. This chapter outlines the Intelligent Manufacturing process, including its key principles, components, challenges, and opportunities. The combination of Machine Learning (ML) techniques and AI enables decision-making, real-time optimisation, and predictive analytics of manufacturing processes, productivity, and quality of products. Robotics and IoT devices play critical roles in enabling automation, data collection, and connectivity within Intelligent Manufacturing environments. Additionally, Digital Twin technology facilitates virtual simulation, modelling, and optimisation of production systems. While Intelligent Manufacturing offers significant benefits, it also presents challenges viz. high investments, integration complexity, and workforce reskilling requirements. Overcoming the challenges requires a holistic approach involving collaboration between industry stakeholders, government agencies, academia, and technology providers. Overall, Intelligent Manufacturing represents a promising future for the manufacturing industry, offering opportunities for innovation, competitiveness, and sustainable growth in a rapidly evolving global economy. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
Intelligent Multi-modal Data Processing
A comprehensive review of the most recent applications of intelligent multi-modal data processing Intelligent Multi-Modal Data Processing contains a review of the most recent applications of data processing. The Editors and contributors noted experts on the topic offer a review of the new and challenging areas of multimedia data processing as well as state-of-the-art algorithms to solve the problems in an intelligent manner. The text provides a clear understanding of the real-life implementation of different statistical theories and explains how to implement various statistical theories. Intelligent Multi-Modal Data Processing is an authoritative guide for developing innovative research ideas for interdisciplinary research practices. Designed as a practical resource, the book contains tables to compare statistical analysis results of a novel technique to that of the state-of-the-art techniques and illustrations in the form of algorithms to establish a pre-processing and/or post-processing technique for model building. The book also contains images that show the efficiency of the algorithm on standard data set. This important book: Includes an in-depth analysis of the state-of-the-art applications of signal and data processing Contains contributions from noted experts in the field Offers information on hybrid differential evolution for optimal multilevel image thresholding Presents a fuzzy decision based multi-objective evolutionary method for video summarisation Written for students of technology and management, computer scientists and professionals in information technology, Intelligent Multi-Modal Data Processing brings together in one volume the range of multi-modal data processing. 2021 John Wiley & Sons Ltd. All rights reserved. -
Intelligent Optimized Delay Algorithm for Improved Quality of Service in Healthcare Social Internet of Things
Internet of Things (IoT) interconnects billions of devices by establishing a network that adheres to International Organization of Standardization (ISO) standards. These devices communicate with each other by sharing data regulated by the application. This is performed to accomplish a task or service that the application demands. The social or human-like behaviors are adapted in the IoT environment forming the Social IoT (SIoT). The SIoT integrates social networks in IoT-connected devices, making them unique and identifiable. Recent advancements in networking, intelligent network management, battery management, remote sensing, sensors, and other related technologies convinced users and designers to adopt IoT even for large-scale applications where the data involved is enormous. Leveraging the advancements in medical IoT, which focuses on healthcare to patients, can improve its service by removing redundant manual processes, long wait times, and providing other automated services. The advancements in real-time healthcare IoT devices and wearables make a strong case for implementing SIoT in the healthcare domain. SIoT in the healthcare domain has the potential to benefit users on a large scale. This chapter comprehends the challenges and solutions of using SIoT in medical and healthcare solutions from a networking quality of service (QoS) perspective. In addition, this chapter compares the intelligent algorithm, which can be used to improve the QoS of SIoT. Achieving higher QoS is necessary for healthcare services, especially while handling data from emergency and intensive care units. These data cannot tolerate errors and delays. Intelligent network management has become unavoidable in the health and medical services to achieve a higher degree of QoS system, which indirectly decreases data transfer time. The data from the sensor devices sent across the network leads to data loss and delay in data transmission due to congestion in the network and gateway devices. The optimized algorithms incorporated with the delay-based algorithm improves the QoS predominantly and reduces the delay in data transfer. Similarly, the particle swarm optimization algorithm allocates resources over the network and dynamically makes the network adapt to increased and reduced data flow, which reduces the delay and improves the QoS. Intelligent optimized delay algorithm (IODA) is proposed to improve the network performance by reducing the delay and using available bandwidth for data transfer in SIoT. 2023 selection and editorial matter, Gururaj H L, Pramod H B, and Gowtham M; individual chapters, the contributors.