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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Manish Prajapati Santos Kumar Baliarsingh Chinmayee Dora Ashutosh Bhoi Jhalak Hota Jasaswi Prasad Mohanty School of Computer Engineering KIIT Deemed to be University Bhubaneswar India Department of Electronics & Communication Engineering Centurion University of Technology and Management Bhubaneswar India Department of Computer Science & Engineering GITAM Deemed to be University Visakhapatnam India

A large language model (LLM) is a trained deep-learning model that understands and generates text in a human-like fashion. Due to the significant advancements of LLM, it becomes a challenging task to distinguish human-written content from artificial intelligence (AI) generated content. In this work, we leverage the machine learning (ML) models to reliably identify whether an essay is authored by a human being or by an LLM. Concerns about LLMs replacing human tasks, especially in education persist. However, optimism remains for their potential as tools to enhance writing skills. An academic worry is LLMs facilitating plagiarism due to their extensive training in text and code datasets. Using diverse texts and unknown generative models, we replicate typical scenarios to encourage feature learning across models. In a study involving human subjects, we demonstrate that the annotation scheme offered by generative textual likelihood ratio (GLTR) enhances the human detection rate of fake text from 74% to 99% without requiring any previous training. GLTR is open source and publicly deployed, already finding widespread use in detecting generated outputs.

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Medical Image Classification for Disease Prediction with the Aid of Deep Learning Approaches 2

Medical Image Classification for Disease Prediction with the...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Pandiyarajan, M. Thimmiaraja, J. Ramasamy, Jayaraj Tiwari, Mohit Shinde, Seira Chakravarthi, M. Kalyan Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India School of Computing Kalasalingam Academy of Research and Academy Tamil Nadu Krishnankovil India Botho University Department of It Botswana Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Computer Science & Engineering BITR India School of Electronics Engineering VIT-AP University Amaravathi India

ISBN: (纸本)9781665437899

Machine learning as well as deep learning algorithms is recently in huge demand in the field of image classification. This research paper illustrates the relevance of these two prospects for making further improvement in the treatment system in the upcoming days. In this context the evolution of the machine learning from its invention to the modern stage has been discussed. The limitations of machine learning are elaborated and as a consequence the emergence of deep learning have been depicted. The study also provides an insight to the different leaning process and their relevance in the field of image classification and image analysis. Opinion of people are gathered about this aspect and presented in a survey format. The overall presentation illustrates a precise discussion about the relevance and evaluation of artificial learning system in the context of image classification which can be used in diagnostic process. The different fields of medical treatment which are seeing benefits of introduction of deep learning network and convolutional neural network are also discussed here. At the end a conclusive summary about the machine learning process and its pros and cons are elaborated to get an idea about the present standing point of machine learning on the field of medical science. © 2022 IEEE.

关键词： Image classification

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A Smart Handling of Bio-Medical Waste and its Segregation with Intelligant Machine Learning Model

A Smart Handling of Bio-Medical Waste and its Segregation wi...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Gupta, Brijendra Sreelatha, P. Shanmathi, M. Philip Bhimavarapu, John John Augustine, P. Sathyarajasekaran, K. Siddhant College of Engineering Sudumbre Department of It Maharashtra Pune India Kpr Institute of Engineering and Technology Dept. of Biomedical Engineering Tamil Nadu Arasur India Electronics and Communications Saveetha Engineering College Kanchipuram Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Electronics and Communication Engineering Andhra Pradesh Guntur India Sri Eshwar College of Engineering Department of Information Technology Tamil Nadu Coimbatore India Vellore Institute of Technology Chennai Campus School of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350323887

The Bio-Medical waste management system organizes everyday medical waste disposal in hospitals. Daily medical waste from hospitals is delivered through it. A separate system is in place for treating medical supplies, including needles, plastic, glassware, medical clothes, expired medications, and human waste. Based on that, they use the Biomedical Waste Management Centre to accept everyday medical waste from their hospitals and appropriately dispose of it. No hospital should ever dispose of medical trash. It is illegal, and the hospital responsible must appropriately separate the medical waste and deliver it to the biomedical waste treatment facility. In this paper, an intelligent machine learning model was proposed to handling the different bio medical wastages and segregate it based on the medical rules. Medical waste disposed of in hospitals is safely transported and incinerated. The proposed model helpful the disposal of such medical waste, which is usually contagious, takes place. © 2023 IEEE.

关键词： Hospitals

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A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection

A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intellig...

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Integrated Intelligence and communication Systems (ICIICS), International Conference on

作者： Khadija Danladi Sankara G. Saritha S. Anitha Elavarasi S. Saradha D. Arul Kumar Masters of Software Engineering Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM's ability to capture temporal dependencies in sequential data and GAN's strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications.

关键词： Adaptation models Accuracy Predictive models Generative adversarial networks Data models Hybrid power systems Cyber threat intelligence computer security Long short term memory Anomaly detection

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A report on Alzheimer's disease Action and Semantical Fluency 8

A report on Alzheimer's disease Action and Semantical Fluenc...

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Julaiha, Ag.Noorul Vasudevan, B. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600 124 India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai600 119 India

ISBN: (纸本)9781665497619

The goal of this paper is to outline the relationship between onset and evident AD, as well as language functions and domains. With rising mortality and an older population, it is projected that almost 5.3 million individuals in India suffer from Alzheimer's disease. Even mild Alzheimer's disease causes linguistic alterations. Speech analysis is the best option since it accurately represents the speaker's brain function and data gathering is reasonably affordable when compared to brain imaging, blood tests, and other methods. Previously, researchers used the MMSE as one of the sources to detect the early start of Alzheimer's disease. We show that the progression from HA to AD is accompanied by a consistent pattern of speech changes, including an increase in duration and vocalization time, an increase in the number of pauses in speech, an emergence of modification in syllabic production, and reduced speech energy and intensity, resulting in speech disorder. Our planned research will provide a complete analysis of speech alterations in MCI and mild AD when compared to healthy ageing (HA), allowing harmful processes to be detected before clinical manifestations of AD. In the proposed work, we evaluate cognitive functions such as noting silence between words and their inaccuracy, which can play a significant role in recognizing the AD stages through emotional intelligence using a deep learning model. We can also add a cognitive phrase matching test to determine the severity of the Alzheimer's disease comprehension loss (AD) through emotional intelligence. © 2022 IEEE.

关键词： Neuroimaging Vocabulary Speech analysis Sociology Semantics Production Speech synthesis

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Theoretical study and analysis of advanced wireless sensor network techniques in Internet of Things (IoT)

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Measurement: Sensors 2024年 33卷

作者： Yamini, B. G, Pradeep D, Kalaiyarasi M, Jayaprakash G, Janani G S, Uthayakumar India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology India Department of Electronics and Communication Engineering Panimalar Engineering College India Department of Information Technology R.M.K. Engineering College India Department of Information Technology R.M.D Engineering College India Department of Electronics and Communication Engineering St.Joseph's Institute of Technology India

Wireless sensor networks those based on the Internet of Things (IoT) have evolved fast in numerous areas in recent years. Devices and sensors may interact with each other without human involvement in the IoT network. An IoT-based system where people and things are connected by wired and wireless technology including Wireless Sensor Networks (WSNs), ZigBee, NFC, RFID, GPRS, LTE, and Bluetooth is now one of the most popular technical ideas. The potential of the Internet of Things (IoT) has received enormous attention from the corporate and scientific communities during the past 10 years. The IoT can provide a number of significant benefits to application spaces. Researchers may be able to show crucial facts about IoT-based smart environmental monitoring systems. IoT has faced a variety of challenges, including authentication, differentiating evidence, accessibility and security and protection. Sociotechnical trust systems have also been examined in the past in various studies (STTS). Existing smart settings are now dealing with serious IoT security, protection, and STTS challenges. Trust-the-board will be highlighted and characterized, as well as how it should be utilized in the Internet of Things. As a result, the potential benefits of data aggregation solutions that use less energy while increasing the organization's lifespan are enormous. IoT-WSN data aggregation methods and computations have been reimagined. Specifically, this article examines the writing with reference to the views of sensor systems administrators in order to preserve energy and gather data. © 2024

关键词： Internet of things

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Automatic Driving Lane Change Safety Prediction Model Based on LSTM 7

Automatic Driving Lane Change Safety Prediction Model Based ...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Sun, Wenjian Pan, Linying Xu, Jingyu Wan, Weixiang Wang, Yong Yantai University Electronic And Information Engineering Shandong Yantai China Trine University Information Studies PhoenixAZ United States Northern Arizona University Computer Information Technology FlagstaffAZ United States University Of Electronic Science And Technology Of China Electronics & Communication Engineering Chengdu China University Of Aberdeen Information Technology Aberdeen United Kingdom

ISBN: (纸本)9798350361445

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes. © 2024 IEEE.

关键词： Trajectories

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： L. Selvam R. Dhamotharan R. Santhana Krishnan J. Relin Francis Raj G. Ram Sankar G. Yamini Department of Computer Science and Engineering SRM Madurai College for Engineering and Technology Pottapalayam Sivaganga India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of English Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies.

关键词： Deep learning Support vector machines Solid modeling Analytical models Three-dimensional displays Accuracy Attention mechanisms Brain modeling Data models Alzheimer's disease

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Indian Agriculture Supply Chain Management using Blockchain Technology and CNN 1

Indian Agriculture Supply Chain Management using Blockchain ...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Indira, D.N.V.S.L.S. Hema Reddy, M. Sri Jyothi, G. Rohith, K. Sravya, K. Jahnavi, G. Department of Information Technology SR Gudlavalleru Engineering College Andhra Pradesh Gudlavlleru521356 India Department of Computer Science and Engineering SR Gudlavalleru Engineering College Andhra Pradesh Gudlavlleru521356 India Department of Electronics and Communication Engineering SR Gudlavalleru Engineering College Andhra Pradesh 521356 India Department of Computer Science and Engineering SRKR Engineering College Chinnamiram Andhra Pradesh Bhimavaram534204 India

ISBN: (纸本)9781665460842

India is a key player in the global food production industry. Indians have been cultivating land since the Indus Valley Civilization. Although historically significant, this country's farmers are starving to death while their counterparts in other countries receive the benefits of their labour to the tune of billions. Lack of proper supply chain management is a contributing factor. It's common knowledge that poorly cared-for farming machinery quickly deteriorates. However, current methods of supply chain management do not enable farmers to track the whereabouts of their commodities, even though the government supplies them with storage facilities. Since no system is in place, they have no idea where their packages are at any one time during the shipping process. Therefore, there is a need for more stringent supply chain management, including checks at each level and double-checking by farmers and government officials to ensure the integrity of goods. This study suggests implementing Blockchain technology, which allows for more transparent information on the status of commodities, to enhance the relationship between producers and consumers. By recording this information on the blockchain, all of the farmers and government employees involved in the shipping process can stay apprised of the operation's finer points. Records can be kept forever in an immutable format. © 2022 IEEE

关键词： Blockchain

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The Least Square QR Method Improves Extreme Learning Machine

The Least Square QR Method Improves Extreme Learning Machine

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2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Subramani, Chinnamuthu Kuppili, Venkatanareshbabu Keshavamurthy, B.N. Jagannath, Ravi Prasad K. Prashanth, Gurusiddappa R. National Institute of Technology Goa Department of Applied Sciences Goa Ponda403401 India National Institute of Technology Goa Department of Computer Science and Engineering Goa Ponda403401 India National Institute of Technology Goa Department of Electronics and Communication Engineering Goa Ponda403401 India

ISBN: (纸本)9798350310061

The Extreme Learning Machine (ELM) is a fast and straightforward learning algorithm designed for single hidden layer feed-forward neural networks (SLFNs). The ELM randomly assigns input weights and calculates the output weights analytically using the Moore-Penrose generalized inverse (MP) (also called Pseudo inverse). However, the MP is not preferable for ill-posed systems as it is sensitive to perturbation and may produce inaccurate solutions. Furthermore, it is computationally expensive as it involves matrix inversion. Regularized ELM (RELM) was proposed to address the limitations of the MP. In RELM, the regularization parameter is the primary factor that controls the classifier's performance. Though the RELM is stable and produces accurate results, finding a n a ppropriate regularization parameter value is tedious and increases the computation. This work proposes a variant of ELM named LSQR-ELM algorithm, which balances the ELM and RELM by maintaining stability and accuracy and avoiding the regularization entirely. The LSQR-ELM is based on the Least Squares QR (LSQR) iterative technique. Since the LSQR finds p a rtial Q R rather than full QR, it reduces unnecessary computation. To verify the proposed algorithm's ability, comparisons are made with several existing methods including ELM, Minimum Residual Method (MT-ELM), SVD-based SG-ELM, Optimally Pruned ELM (OP-ELM), and K-nearest neighbor (KNN). Extensive experimen-tation on various datasets demonstrates that the LSQR-ELM algorithm outperforms ELM, SG-ELM, OP-ELM, and KNN on most datasets. Moreover, it exhibits comparable performance to MT-ELM while requiring significantly f e wer computations. © 2023 IEEE.

关键词： Learning algorithms

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