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Artificial Ecosystem-Based Optimization for Optimal Location and Sizing of Solar Photovoltaic Distribution Generation in Agriculture Feeders
In this paper, an efficient nature-inspired meta-heuristic algorithm called artificial ecosystem-based optimization (AEO) is proposed for solving optimal locations and sizes of solar photovoltaic (SPV) systems problem in radial distribution system (RDS) towards minimization of grid dependency and greenhouse gas (GHG) emission. Considering loss minimization as main objective function, the location and size of solar photovoltaic systems (SPV) are optimized using AEO algorithm. The results on Indian practical 22-bus agriculture feeder and 28-bus rural feeders are highlighted the need of optimally distributed SPV systems for maintaining minimal grid dependency and reduced GHG emission from conventional energy (CE) sources. Moreover, the results of AEO have been compared with different heuristic approaches and highlighted its superiority in terms of convergence characteristics and redundancy features in solving the complex, nonlinear, multi-variable optimization problems in real time. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Artificial immune system based frameworks and its application in cyber immune system: A comprehensive review
Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research. 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
Artificial intelligence talent acquisition in HEIs recruitments
Purpose: The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA). Design/methodology/approach: A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management. Findings: The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA. Originality/value: AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition. 2024, Emerald Publishing Limited. -
Artificial Intelligence - Based Steganography Model for Social Media Data Set
Steganography, one of the data security mechanisms under our investigation, shields legitimate messages from hackers and spies by employing data hiding. Data protection is newlinecurrently the top priority due to the signifcant advancements in information technology due to high-security concerns. Traditional techniques for maintaining data confdentiality include steganography and cryptography; the distinction is that steganography does not naturally arouse suspicion, whereas cryptography does. Traditional linguistic steganographic methods suffer from limitations in automation, accuracy, and the volume of concealed text. The robustness and undetectability properties of these approaches also require improvement. Third-party vulnerability is often too high for conventional techniques to handle. Artifcial intelligence is increasingly replacing traditional model creation in steganography. Despite the fact that steganography ensures security, information sent over online social networks (OSN) is plainly not safe. Steganography along newlinewith encryption can make a difference with regard to privacy of information in transit. newlineThe research study aims to build algorithms or models and assess steganography s robustness, security, undetectability, and embedding ability. Two distinct types of data newlineconcealing employed for investigation: text and image. The results were encouraging newlinewhen we initially tested our Laplacian model using image steganography and compared newlinewith benchmark methods. The second experiment, which is based on AI, generates the cover text using secret information, examines the security and robustness of steganography. The study compared suggested text steganography model, 3-bit data concealing, with other existing techniques in order to ascertain the undetectability factor. The frst experiment used MATLAB tools, and the second used the markovify python module, RNN (Recurrent Neural networks), and the Huffman tree. Further format-based steganography methods utilized in the following experiment. -
Artificial Intelligence (AI) in CRM (Customer Relationship Management): A Sentiment Analysis Approach
The use of customer relationship management (CRM) in marketing is examined in this essay. It looks at how CRM makes it possible to use reviews, integrate AI, conduct marketing in real time, and conduct more regular marketing operations. CRM tactics are illustrated through case studies of businesses like Uber, T-Mobile, Amazon, Apple, and Apple. CRM offers centralized data, better marketing and sales, and better customer support. There is also a discussion of the ethical, private, security, adoption, and scalability challenges of AI in CRM. In general, CRM makes data-driven decisions and customer insights easier to achieve to increase growth, loyalty, and engagement. 2024 IEEE. -
Artificial Intelligence & Automation: Opportunities and Challenges
Artificial Intelligence (AI) and Automation innovation are growing at a steady rate that are changing organizations and bringing efficiency and adding to the economic development. The utilization of AI and robotization will likewise help improve different areas from wellbeing to horticulture. Furthermore, utilizing Automation and Artificial Intelligence would, follow the schedule, transform the idea of work and the working environment itself. For sure, machines will actually do large numbers of the undertakings typically done by people, just as supplement manual work and play out certain errands that an individual wouldn't have the option to do. Consequently, AI and mechanization have a great deal to bring to organizations and enterprises worldwide. This research paper comes up with a rundown through the blooming of Artificial Intelligence and Automation. We explored the existing potentiality of cognitive emerging technologies. This paper outlines the discussion about artificial intelligence and automation technologies and an overview of the applications. 2023 American Institute of Physics Inc.. All rights reserved. -
Artificial Intelligence & Data Warehouse Regional Human Resource Management Decision Support System
High-quality data is utilized to make informed decisions that effectively help to successfully safeguard our environment. When there is an abundance of information that is both heterogeneous in nature (coming from a wide variety of fields or sources) and of unknown quality, various problems may occur. Furthermore, the problem's dynamic nature also imposes some other complications. In order to deal with such complications, the central role played by supercomputers in the modern environment is to promote protection initiatives like monitoring, data analysis, communication, and information storage and retrieval. In current days, the higher dependency on the data management process forced the developers to integrate and enhance all these initiatives with Artificial Intelligence knowledge-based techniques so that smart systems can be utilized by a vast number of people. In this context, this study has illustrated how Artificial Intelligence methods have changed the nature of Environmental Decision Support Systems (EDSS) over the course of the last two decades. The strengths that an EDSS should exhibit have been emphasized in this review. In the final section, we look at some of the more innovative solutions used for various environmental issues. 2022 IEEE. -
Artificial Intelligence and Deep Learning Based Brain Tumor Detection Using Image Processing
In the field of medical science, applications that are particularly used for diagnostic purposes, are used in the detection of brain tumors since detecting an error in MRI scanning is becoming a major task for radiologists and requires a lot of their focus. Flaws that are prevalent during tumor detection must be taken care of to avoid further complications. MRI scanning is one of the most recently developing technologies. The radiologist is a key player in the identification of the brain tumor. Radiologists have to check every image perfectly to avoid the errors in identifying the brain tumor. There is a probability that sometimes cerebral fluid may also appear as mass tissue during the MRI scan. The model that is proposed in this research uses a machine learning algorithm which helps to improve the validity of the classification of the images that are taken in MRI scans. The study focuses on having an automated system that carries out an essential role in determining whether a lump is present in the brain or not. The study tries to resolve the primary flaws in detection necessary to evade further complications in MRI images in brain detection. The main aim of this study is to train the algorithm in a more extensive dataset and to check the patient-level validity with the help of various new datasets. 2023 IEEE. -
Artificial intelligence and deep learning based driverless cars to reduce the road accident, death rate using python /
Patent Number: 202221047470, Applicant: Rashel Sarkar.
2% of global deaths each year are caused by automobile accidents. This corresponds to around 3,287 each day, or 1,300,000 per year. 20 million to 50 million people are seriously injured in automobile accidents annually. Why do these recurring problems persist People do make errors. One careless or foolish action is all it takes to transform a safe drive into one that could kill someone. This holds true regardless of whether the driver is preoccupied, intoxicated, or simply careless or irresponsible. In terms of technology, Artificial Intelligence (AI) has always been ahead of the curve. -
Artificial intelligence and deep learning based driverless cars to reduce the road accident, death rate using python /
Patent Number: 202221047470, Applicant: Rashel Sarkar.
2% of global deaths each year are caused by automobile accidents. This corresponds to around 3,287 each day, or 1,300,000 per year. 20 million to 50 million people are seriously injured in automobile accidents annually. Why do these recurring problems persist People do make errors. One careless or foolish action is all it takes to transform a safe drive into one that could kill someone. This holds true regardless of whether the driver is preoccupied, intoxicated, or simply careless or irresponsible. In terms of technology, Artificial Intelligence (AI) has always been ahead of the curve. -
Artificial Intelligence and Internet of Things readiness: Inclination for hotels to support a sustainable environment
The idea of Smart Cities has been one of the key driving factors for the urban transformation to a low carbon climate, sustainable economy and mobility in recent years because of the alarming situation of global warming. One of the industries with swift growth is hotel sector and hence is one of the key contributors to carbon emission and leaves environment footprints. The new emerging concept of sustainable tourism is envisaged as an important part of the Smart Cities paradigm. Improving sustainability by saving energy is becoming a primary task toady for many hotels. A great opportunity is provided by Artificial Intelligence (AI) and Internet of Things (IoT) to assimilate different systems on a platform by encouraging and assisting hotel guests to operate through single device and optimizing hotel operations. Current research focuses to identify the strategic positions of a hotel in terms of sustainability, AI and IoT technology. Components that will be considered by Hotels for the strategic intention of adopting AI and IoT for environmental sustainability. Different development and modification needed to be taken if management wants high sustainability readiness and/or IoT readiness. This conceptual paper constructs on the comprehensive study and systematic review of different area where the hotels can feasibly implement AI and IoT for improving sustainably. 2021 Elsevier Inc. All rights reserved. -
Artificial Intelligence and Machine Learning Combined Security Enhancement Using ENIGMA
Enigma is a relatively new and emerging field that has the potential to bring significant benefits to the way contracts are executed and managed. The integration of Artificial Intelligence (AI) into smart contract technology can automate repetitive tasks, reduce the need for human intervention, improve decision-making, and provide transparency and trust. It can also provide more flexibility, handle more complex tasks, learn from past experiences, have predictive capabilities, and have human oversight and intervention. All these features make Enigma contracts more advanced than traditional smart contracts. AI-powered smart contracts, or Enigma contracts, can also improve contract execution, increase efficiency, facilitate better negotiation, and facilitate automated dispute resolution. However, as the technology is still in its early stages, major challenges and risks can adopted but the need for robust security. The potential for AI is to make decisions that are not in the best interests of its parties. Despite these challenges, the potential benefits of AI-powered smart contracts make them an area of on-going research and development that is worth exploring further. Enigma can be used or applied in various fields, and can be used to secure the sensitive information by applying robust security system. Enigma contract is a AI powered smart contract which is used to automate decision-making processes and improve its efficiency, Enigma as the name suggest it is a complex security network which has the potential to revolutionize the security system by increasing efficiency. 2023 IEEE. -
Artificial Intelligence and Machine Learning-Based Systems for Controlling Medical Robot Beds for Preventing Bedsores
Artificial Intelligence is one of the most important technologies of the modern world which is continuously changing the dimensions of almost every sector. AI and IoT have together resulted in multiple outstanding technological innovations which have also impacted the healthcare sector massively. This study has critically focused on the role of AI and robotics in the treatment outcomes for patients. This study has done deep research regarding the role of automated beds in reducing pressure ulcers or bed sores among patients who are recovering from any chronic disease. This entire study has secondary qualitative data collection for analyzing the design and microcontroller systems in automated beds. This has provided a detailed data analysis with relevant equations and tables for reaching its proposed outcomes. 2022 IEEE. -
Artificial intelligence and service marketing innovation
The integration of artificial intelligence (AI) into service marketing in India is expected to significantly impact marketing strategies and economic dynamics. The emphasis on personalization, automation, predictive analytics, and chatbots will enhance customer engagement and brand loyalty, leading to increased sales and revenue. Automation of marketing workflows will streamline operations, improve efficiency, and foster business growth. AI's predictive analytics capabilities will help businesses make informed decisions about their marketing strategies, particularly in a diverse market like India. AI-driven chatbots will enhance customer satisfaction and engagement, contributing to positive brand perception and loyalty. However, there may be concerns about job displacement, particularly in routine tasks. The growth of AI-driven service marketing can contribute to the development of a technologydriven ecosystem in India, attracting investments, fostering entrepreneurship, and stimulating innovation. 2024 by IGI Global. All rights reserved. -
Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE. -
Artificial Intelligence Based Computational Framework for Identification and Classification of Interstitial Lung Diseases Using HRCT Images
Interstitial Lung Diseases (ILDs) refer to a wide array of respiratory disorders characterised via infection and scarring of the lung's interstitial tissue. These conditions affect the space within the air sacs, compromising the lungs' ability to expand and contract properly. ILDs manifest with a range of symptoms, including persistent cough, shortness of breath, and fatigue. Diagnosis of ILDs often involves imaging methods, mainly High-Resolution Computed Tomography (HRCT), to assess lung abnormalities. ILDs can have lasting effects on respiratory function, leading to progressive fibrosis. The primary obstacle in identifying ILDs lies in the diverse array of symptoms they present, making it challenging to distinguish them from other pulmonary disorders. The HRCT is a commonly employed method in ILD diagnosis. These images provide a detailed depiction of lung tissue, revealing its size, shape, and any notable abnormalities or changes. Moreover, HRCT plays a crucial role in monitoring disease progression over time. Deep Learning (DL) excels in detecting patterns in intricate medical images that may pose challenges for traditional methods. Moreover, DL algorithms exhibit the ability to identify subtle changes in medical images indicative of pathology, and they can automate object detection tasks. The application of DL in medical contexts can enrich the precision and rapidity of diagnoses. In this research aimed at improving the accuracy of artificial intelligence AI-based ILD identification, we harnessed the benefits of deep learning, employing full-training, Transfer Learning (TL), and ensemble voting techniques. Our approach involved the construction of three Convolutional Neural Networks (CNNs) from scratch for ILD detection. Additionally, we customized models named InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50 for both full-training and TL strategies. This comprehensive methodology aimed to take benefits of DL architectures to enhance the precision of ILD identification in medical imaging. Both the first dataset consisting of HRCT images and the second dataset comprising Chest X-ray were employed in our study. However, during the initial training phase of the TL models, we utilized pre-trained ImageNet weights. To enhance performance, modifications were made to the classification layers of all five models for both TL and full-training processes. To further improve training outcomes, a soft-voting ensemble approach was employed. The ensemble, combining the predictions of all three newly developed CNN models (ILDNetV1, ILDNetV2 and ILDNetV3), and ILDNetV1 achieved the highest test accuracy at 98.14%. Additionally, we incorporated machine learning (ML) models, including Logistic Regression, BayesNet, RandomForest, Multilayer Perceptron (MLP), and J48, using statistical measurements derived from HRCT images. Our study introduces a novel AI-based system for predicting ILD categories. This system demonstrated superior performance on unseen data by leveraging the results from the newly constructed CNNs, transfer learning, and ML models. This comprehensive approach holds promise for advancing ILD category prediction, providing a more robust and accurate tool for medical diagnosis and decision- making. -
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Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE. -
Artificial Intelligence based Semantic Text Similarity for RAP Lyrics
Data mining is the primary method of gathering large volumes of knowledge. To make use of such data to implementation requires the use of effective machine learning strategies. Semantic Textual Similarity is one of the primary machine learning strategies. At its core, semantic textual similarity is the identification of words with similar context. Initial work in STS involved text reuse, word search among others. The proposed research work uses a specific method of determining textual similarity using Google's Word2Vec framework and the Continuous-bag-of-words algorithm for identifying word similarity in rap records. A large data set consisting of over 50,000 rap records is used. The key aspect of proposed methodology is to determine the words with similar context and cluster them into different word clusters also called bags. To achieve the desired result, the dataset is first processed to obtain the features. Once the features are selected, a model is generated by passing the data onto the Word2Vec framework. The research work on semantic textual similarity was repeated across three different runs, with the data set size changing in every run. At the end of each the accuracy of similarity obtained by the model was determined. The current research work has achieved average accuracy as 85%. 2020 IEEE.