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machine Learning for Forecasting Depression and Anxiety in University Students 1

8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Comparative analysis of various web crawler algorithms

arXiv

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arXiv 2023年

作者： Nithin, T.K. Chandana, S. Barani, G. Dharani, Chavva Karishma, m.S. Integrated M.Tech Computer Science and Engineering VIT Vellore India

This paper describes and evaluates five different crawling algorithms that we have implemented within our evaluation framework: Shark search, Priority Based Focused Crawler,Naive Bayes,Breadth-First, Depth-First and c... 详细信息

关键词： Web crawler

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Effective Crop Yield Prediction Using Gradient Boosting to Improve Agricultural Outcomes 1

Effective Crop Yield Prediction Using Gradient Boosting to I...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Pradeep, G. Rayen, T Dureen V Pushpalatha, A. Rani, P. Kavitha Sri Krishna College of Engineering and Technology Department of M.Tech. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350336009

Crop production forecasting is a huge challenge nowadays, resulting in inaccurate results such as food shortages, economic instability, inefficient resource allocation, environmental impact, and lower farmer profitability. Our proposed machine-learning algorithm forecasting yield can help address these difficulties and enhance agricultural outcomes. Crop yield prediction is used to estimate the potential harvest of crops, providing valuable information to farmers, policymakers, and agribusinesses for planning, resource management, and making informed crop production decisions. It helps to improve food security, reduce food waste, and increase the efficiency of food production. Gradient Boosting Agricultural Yield Prediction is a machine learning approach that employs decision trees and gradient descent optimization to create accurate crop yield predictions. This approach and strategy are useful in predicting crop yields. They can assist farmers and agricultural organizations in making better-educated planting, harvesting, and resource allocation decisions. The results of crop yield prediction based on gradient boosting with an accuracy rate of 87.2%, precision of0.84, recall ofO.90, and F1-Score of0.87 indicate that the model is making accurate predictions about crop yields with a good balance of precision and recall. Our work suggests that the model performs efficiently and makes accurate predictions for crop yields. It increases crop production prediction, which improves decision-making, increases efficiency, effectively allocates resources, supports planning, and reduces agriculture's environmental impact. It has a tremendous impact on the agriculture sector because it promotes sustainability, reduces waste, and improves overall performance. © 2023 IEEE.

关键词： Efficiency

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IoT-Enabled Sleep monitoring Wearables: Advancements in Tracking and Analysis 2

IoT-Enabled Sleep Monitoring Wearables: Advancements in Trac...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Thinakaran, K. Baskar, V. Vijaya Shariff, m. Ahsan Chervith, manduva Raghavendra, G. Velmurugan, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Sathyabama Institute of Science and Technology School of Electrical and Electronics Engineering Tamil Nadu Chennai India Sri Sai Ram Engineering College M.Tech Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil NaduKattankulathur Chennai India Mother Theresa Institute of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Palamaner India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

The importance of sleep to a person's health and well-being cannot be overstated. Sleep monitoring and analysis control has been transformed by the combination of Internet of Things (IoT) technologies and wearable devices in recent years. This system analyzes the improvements achieved in sleep monitoring wearable enabled by IoT, providing a thorough and discreet method of evaluating sleep activities. Accelerometers and heart rate monitors are at the heart of these wearables, which also have complex processing units and connectivity modules. Insightful sleep quality analysis, precise sleep stage separation, and real-time data collecting are all made possible by this complementary relationship. Wearables may track sleep patterns, interruptions, and effectiveness by recording motion, heart rate variability, and brain activity through various sensors. Smart cloud interfaces that provide sleep metrics and trends improve the user experience and allow a deeper analysis of a person's sleeping habits. In addition, these wearables may easily connect to smart home systems, enhancing sleep quality by modifying the house's lighting and audio settings depending on the individual's sleeping habits. The data gathered by these wearables has wider uses beyond personal sleep monitoring, such as helping the system better understand and treat sleep conditions. © 2024 IEEE.

关键词： Internet of things

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Automated Semantic Interpretations in Web Applications via Cloud Computing Aspects

Automated Semantic Interpretations in Web Applications via C...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Preethi, D. mehta, Poornima Sood, Devendra Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of It And M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The creation and execution of applications in several disciplines have been greatly aided by the integration of cloud computing and semantic web technologies. Although they are independent entities, they may also be combined in many ways to provide answers - a subject that has been thoroughly researched lately. The number of 'new cloud providers, applications, facilities, management systems, and data' has significantly increased in recent years. The complexity that has resulted from this expansion emphasises the need for new technology in order to efficiently manage and support these varied and many services and resources. As a result, difficulties with 'cloud services and resources' transferability, compatibility, security, choice, negotiation, identity, and specification might arise. Semantic technologies are a vital tool for reassessing these issues because of their enormous 'cloud computing' capacity. This study uses a variety of sources to examine and evaluate the impact of 'Semantic-Web technology' in the cloud. moreover, a cloud-based engagement strategy shows how we may leverage the features and advantages of cloud computing to build the semantic web and provide automatic semantic annotations to online applications on a large scale. © 2024 IEEE.

关键词： Semantic Web

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An Automatic Error Recognition approach for machine Translation Results based on Deep Learning 2

An Automatic Error Recognition approach for Machine Translat...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Rekha, m.N. Prasad, mudarakola Lakshmi mukherjee, Saptarshi Nikam, Sudam Vasant Sharma, Swati Reddy, Pundru Chandra Shaker Jss Science and Technology University Electrical and Electronics Engineering Sri Jayachamarajendra College of Engineering Karnataka Mysore India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Bharati Vidyapeeth College of Engineering Instrumentation Engineering Navi Mumbai India Presidency University School of Computer Science Engineering and Information Science Karnataka Bangalore India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India

ISBN: (纸本)9798350387933

At present, the speedy growth of natural-language-processing(NLP) has produced considerable evolution for the topic of machine-translation. There have been many deep neural network-based machine-translation(mT) methods that have become increasingly broad. Still, there currently lacks reliable automatic-error-detection(AED) systems for mT outcomes. One important metric for gauging the efficacy of an engineering English translation application is the accuracy of the corresponding deep learning model's translation. This research presents a deep learning-based solution for automatic mistake identification in machine translation results as a means of closing this gap. In this research, we propose a deep generative model to synthesis the training data that will be utilized to train a model to correct grammatical errors in English used in international trade. Next, the learner's corpus is corrected using the grammatical fault alteration approach, and 'error-correct' sentence pairs are created using the exacted aim sentences and by hand interpreted standard sentences and fed back to the error generation technique for retraining. The model's error detection and correction capabilities are enhanced through the establishment of a connection amid the grammatical error recognition and correction model. The suggested error recognition method has been shown to considerably increase the stealthiest of the trigger, guarantee the efficacy of the backdoor assault, and make the trigger resistant to specific data augmentation operations in experiments using datasets like GTRSB. © 2024 IEEE.

关键词： Error detection

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D mRI Images 1

Brain Tumor Classification Using UNet Deep Neural Networks f...

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Shaik, mohammad Khaja Kalpana, D. Sesadri, U. mukherjee, Saptarshi Dastagiraiah, C. Reddy, Pundru Chandra Shaker St. Ann's College of Engineering and Technology Electronics and Communication Engineering Andhra Pradesh Chirala India Departmet of CSE-Data Science India Vardhaman College of Engineering Computer Science and Engineering Telanagana Hyderabad India Maulana Abul Kalam Azad University of Technology M.Tech-Bioinformatics West Bengal India Anurag University Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798350371802

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of mRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(mL) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of mRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top. © 2024 IEEE.

关键词： magnetic resonance imaging

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Secure and Transparent Client Fund Collection Protocol Enabled by Blockchain 4

Secure and Transparent Client Fund Collection Protocol Enabl...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Beevi, L Sherin Joe Prathap, P.m. Dani, W Vinil Abinaya Lakshmi, B.G. Chowdary, GurramRenuka Devi, Hema R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India R.M.D. Engineering College Dept. of Computer Science and Engineering Kavaraipettai India

ISBN: (纸本)9798331540364

Blockchain technology has evolved as a formidable instrument in the financial technology space, providing unrivalled security, transparency, and efficiency. This article presents a groundbreaking approach for safe and transparent client fund collection using blockchain technology. Addressing the flaws of traditional money collecting systems, such as security risks, opacity, and inefficiency, the protocol uses blockchain's inherent properties to revolutionize fund collection. The protocol, initiated by the client, permits fund collection requests through a secure client interface, with the blockchain network authenticating transactions using cryptographic techniques to ensure only authorized transactions are performed and immutably recorded on the blockchain.. Notably, the protocol's openness and security, which are supported by strong encryption algorithms and cryptographic protocols, reduce the danger of fraud and embezzlement while establishing trust in stakeholders. This protocol, which leverages blockchain capabilities, provides a disruptive solution for money collection, increasing responsibility and confidence in the financial industry. © 2024 IEEE.

关键词： Authentication Protocol

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Enhancing Food Supply Chain Transparency and Agricultural Practices through Blockchain technology 5

Enhancing Food Supply Chain Transparency and Agricultural Pr...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Dinesh Kumar, K. Vennila, G. manoj Kumar, D.S. Jasmine, J. Jeno Govindharaj, I. Balamurugan, S. Vel Tech University Department of Computer Science and Engineering Chennai India Mohan Babu University Department of Aiml Chennai India Chennai India R.M.K Engineering College Department of Computer Science and Engineering Chennai India School of Computing and It Bengaluru India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376685

Blockchain technology emerged to be one of the smart solutions to ease the functionality of many real time applications due to its decentralized nature, high security and distributed decentralized ledger. many people have started questioning about the production process of food product that will ultimately be sold at market and retail shops. Blockchain provides viable solution to track the complete process of crop production from harvesting to food products. Therefore, agro business are increasingly searching for supply chain management application that may help to improve the standard of safe food handling, quality and traceability through the agricultural supply chains. Implementing blockchain into the food supply chain makes it more transparent in its operations thus boosting visibility as well as traceability of items from farms to grocery shops. Furthermore, this action mitigates financial risks and promotes all-inclusive commerce. The proposed method able to avoids trusted centralized authorities, middlemen or transaction records but rather permitting direct interaction among peers by having their own personal blockchains. The systematic capturing of transaction at each stage of manufacturing cycle with transparency in the transactions with blockchain security. The aim of this research work to enable the consumers to access the product information from the origin of their food product, including its environmental footprints, storage, food processing etc., beyond food supply chain traceability and transparency this research work aims to find new applications in farming. These may include crop management, food authentication, crop insurance etc., overall to improve the agriculture practices. © 2024 IEEE.

关键词： Agri-food

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An IoT Based Framework for monitoring Power Consumption in Smart Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Raghavendra, R. Chauhan, Nidhi mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Information Technology & M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The electrical power system grid in India, commonly referred to as the power grid, has been in operation for an extensive duration. During this time period, there were no significant advancements or alterations documented in the electricity grid infrastructure. The need for electrical power consumption is seeing a significant increase, but the current grid infrastructure is unable to meet these growing demands. In order to meet the demands of future power load, it is imperative to develop an enhanced system that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power management system and explains its automation and functionality by utilising smart grid and IoT technologies. The research suggests a method for keeping an eye on consumer power use and controlling the use of electrical devices by putting in place a system that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problems with power usage. Future system improvements that are region-specific and intended to reduce loads and maximise system performance are made possible by the data collected. © 2024 IEEE.

关键词： Internet of things

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