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Innovation in technology (INOCON), IEEE International Conference for

作者： A. Dharshan Purushottam Kumar S. Ravimaran U Srinivasulu Reddy Department of Artificial Intelligence and Data Science Saranathan College of Engineering Trichy India CoE in Artificial Intelligence Machine Learning & Data Analytics Lab National Institute of Technology Trichy India Department of Computer Applications Machine Learning & Data Analytics Lab National Institute of Technology Trichy India

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems.

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Review of machine learning and Deep learning algorithms for Personality traits classification

Review of Machine Learning and Deep Learning algorithms for ...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Satish N Bhadane Prateek Verma Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits" It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of p

关键词： Industries Deep learning Video on demand Recurrent neural networks Social networking (online) Reviews Psychology Sea measurements Predictive models Web sites

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The Future of Driving: A Review on the Combination of artificial intelligence and Autonomous Vehicles

The Future of Driving: A Review on the Combination of Artifi...

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artificial intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Nikita Zade Mayur Gawande Prateek Verma Swapnil Gundewar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Sawangi Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Sawangi Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The convergence of artificial intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability.

关键词： Ethics machine learning algorithms Reviews Navigation Decision making Sensor fusion Safety artificial intelligence Socioeconomics Automotive engineering

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Investigation of Hand Gesture Recognition and Script Generation Models

Investigation of Hand Gesture Recognition and Script Generat...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： M Udhayakumar Titus Issac J Sebastian Terance Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

The communication between differently challenged persons especially deaf and mute persons to a common man is challenging. The research work proposes to investigate various deep learning models to enhance communication between the deaf and persons who may not know sign language. The work uses computer vision technology for real-time hand gesture identification and natural language processing to produce coherent and understandable words This article investigates the feasibility of applying deep learning techniques like convolutional neural networks, xgboost and random forest to recognize hand gestures in real-time. The models are trained on a dataset of hand gestures and are investigated to identify hand gestures in real-time. The model can recognize a wide range of hand motions, from simple ones like pointing and waving to complex ones involving several fingers. The system creates sentences that are consistent with the hand gesture's intended meaning once it has been identified. Based on established rules and grammar suggestions, the model tries to generate appropriate sentences while taking the context of the conversation into account. The proposed work would be highly beneficial to researchers and target audiences who have trouble expressing themselves in a quick, efficient way to communicate.

关键词： Deep learning Sign language Target recognition Computational modeling Oral communication Real-time systems Natural language processing

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System for Detecting Intrusions using Raspberry PI

System for Detecting Intrusions using Raspberry PI

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Mathew, Dennise Kirubasri, G. Vijay, K. Eugene Berna, I. Sowmia, K.R. Baselios MathewsII College of Engineering Department of Computer Science and Engineering Kollam India Sona College of Technology Department of Computer Science and Engineering Salem India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350348217

Home invasions and thefts have gotten far too frequent in recent years. However, it is uncommon for people to be familiar with different systems, such as intrusion detectors, surveillance systems, and so on. The average person finds it challenging to maintain his home secure as a result. CCTV cameras are expensive in surveillance since they employ computers. It sets aside an excessive amount of space for ongoing recording and needs personnel to find irregularities. Our goal is to develop a comprehensive home security system that will help homeowners protect their properties from such disasters. The Raspberry Pi4-based Home Security System project aims to let someone secure their home against burglary and theft in one go. However, the Raspberry Pi system is significantly less expensive, has greater resolution, and has capabilities that use less power than the current system. Here, infrared (IR) sensors are used as straightforward yet effective triggers for human presence. For tiny confidential data area surveillance, this solution is appropriate. i.e., a home office, a bank restroom, etc. The image is taken by the camera and momentarily saved in the Raspberry Pi module whenever motion is detected by the PIR sensor within the room. Applications built on the Internet of Things can be used remotely to monitor activities and receive alerts when motion is detected. © 2023 IEEE.

关键词： Cameras

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Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using machine learning and Location Services

Conversational Symptom Checker Chatbot for Disease Predictio...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Rushikesh Burle Swapnil Gundewar Sanskruti Gaurkhede Bhushan Fulkar Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Computer Science And Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Mahartashtra India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

A chatbot, sometimes known as a conversational agent, is software that uses natural language to interact with people. Travelling to and from a hospital for treatment takes a lot of time for people with hectic schedules, which makes many of them disregard their health issues. People frequently postpone going to the hospital for small problems that might develop into serious illnesses. For these people, interacting with chatbots to get helpful medical information is a cost-effective, time-efficient, and convenient option. This paper suggests a framework for a machine learning-based chatbot that combines natural language, machine learning, and statistical analysis. gaining knowledge of the technologies in a Spark cluster to help create an intelligent chatbot. In this work, we conducted a thorough analysis of current literature, looking at a large number of chatbot-related papers over the last five years. Next, we introduced many related tasks to our chatbot, which uses user symptoms to predict diseases and, depending on the user’s location, recommends hospitals in the area. Users will be able to easily engage with the chatbot using the system’s online interface.

关键词： Recurrent neural networks Hospitals Statistical analysis machine learning Chatbots Tokenization Sparks Medical diagnostic imaging Long short term memory Diseases

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AI-Powered Early Detection of Sepsis using Infrared Image Multi-Target based Faster Region-based Convolutional Neural Network 1

AI-Powered Early Detection of Sepsis using Infrared Image Mu...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Almusawi, Muntather Kamma, Vidya Madhavi, Arelli Jeyanthi, S. Santhiya, B. The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Neil Gogte Institute of Technology Rangareddy Department of Computer Science and Engineering Hyderabad India Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Artificial Intelligence & Machine Learning Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Applications Chennai India Amman Institute of Technology Sathyamangalam Department of Information Technology Bannari India

ISBN: (纸本)9798350352931

Sepsis is a complex and heterogeneous syndrome that continues to pose significant challenges to healthcare globally, particularly in realm of Al-powered early detection. The increasing demand for Intensive Care Unit (ICU) admissions and associated rising costs have urged the need for effective management strategies. The problem of the sepsis addresses the issues of misclassification and insurance organizations that arise from inefficient the medical recorder. This paper, proposed Infrared Image multi-target based Faster Region-based Convolutional Neural Network (FR-CNN) technique efficiently achieve better performance in the sepsis detection. Initially data obtained from the Best care database at KAMC with a focus on patient care in ICU through analysis of medical records. The pre-processing utilized min-max normalization and enhance the image quality which is uniformly to process the feature vector. The FR-CNN architecture is involved to identify the sepsis in multiple within infrared image. The model is trained using labelled data that include various infrared image depicting sepsis related feature and detection the complex patterns associated with sepsis, which is achieve better accuracy. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall and f1-score metrics on Best care database. © 2024 IEEE.

关键词： Convolutional neural networks

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An Effective Web Scripting Algorithm for Real Time Data Processing

An Effective Web Scripting Algorithm for Real Time Data Proc...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Harini. S J. Praveenchandar Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

There are various online web sites available nowadays. People are using online web sites for shopping etc. So take a particular website to scrape the data present in it. Choose *** to fetch the price of the product and display it according to the user's feed. Reach that website and the price of the product may changes according to the seasons, festivals, or some special events. So it fetches the price of the current date and provide it to the user's feed. Additionally, the date and time for the particular product is included. Here BeautifulSoup and pyspark are used. Here they use scraping tactics, specifically, in the realm of online advertising. In order to do this, this study suggests a collaborative filtering-based Web advertising system that makes use of Web scraping to identify the most pertinent ads for a generic Web page. Scrapers are especially interested in converting unstructured material and storing it in organized databases. Web scraping is a software method that gathers data from websites by using appropriate web browsers or low-level Hyper Text Transfer Protocol (HTTP) to mimic human exploration of the World Wide Web 1. It is closely associated with web indexing, which extracts information from the web using bots that have been designed by humans. The primary goal of Web Scraping is to convert unstructured data-typically in HTML format-into structured data that can be stored and handled in a centralized database 1. This technique is frequently utilized for a number of different tasks, including pricing comparison, mashups or mix-ups of webpages, weather prediction information, webpage integration, and webpage interface change detection. Some of the challenges are IP Blocking and Rate limiting, Dynamic content Loading, etc.

关键词： Databases Heuristic algorithms Web pages Weather forecasting Web sites Feeds Data mining

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Detection of pulmonary embolism with improved Xception architecture

Detection of pulmonary embolism with improved Xception archi...

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International Conference on AI and the Digital Economy (CADE 2024)

作者： Getzi Jeba Leelipushpam Paulraj P. Joyce Beryl Princess Immanuel Johnraja Jebadurai S. Celshia Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences India

ISBN: (数字)9781837241842

The blocking of arteries in the lungs through blood clots is known as pulmonary embolism. This blood vessel block usually originates in the legs and travels to the lungs causing fatal damage. An imaging test called Computed Tomography (CT) pulmonary angiography scans the lungs for blood clots. The preferred method for identifying pulmonary embolism is CT pulmonary angiography. Manual examination by radiologists increases the workload, consumes more time and also requires experience for correct examination. The objective of this paper is to propose an improved Xception architecture based on Convolutional Neural Networks. The existing architecture is improvised by replacing the Relu activation function with the Swish activation function. This improves the feature extraction of the algorithm thereby improving efficiency in terms of loss and accuracy. The algorithm has been trained and tested using the standard RSNA-STR Pulmonary Embolism CT Dataset. It is evident from the experimental results that our model achieves an improvement of 3.4% and 1.4% in terms of accuracy and loss respectively.

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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

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Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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