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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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Reliable and Efficient Routing for Water Quality monitoring in Underwater WSN 2

Reliable and Efficient Routing for Water Quality Monitoring ...

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

作者： Fernandes, J. Bennilo Gopi, B. Shariff, m. Ahsan Pasha, mohammed Rehaman Srinivasan, S. maniraj, S.P. Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Tamil Nadu Rasipuram India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

monitoring water quality has become crucial to provide clean and safe water. The traditional monitoring procedures are useless because they take a lot of function, require a long time, and cannot give results instantly to promote proactive water contamination reactions. This paper introduces a Reliable and efficient routing for Water Quality monitoring (REWQ) in Underwater Wireless Sensor Networks (UWSNs). This mechanism utilizes the Cuckoo search (CS) algorithm to select the next hop to transmit the data from the sender to the surface region sink. The CS algorithm computes the fitness function by node energy usage, depth value and loss ratio. The proposed CS algorithm aims to improve reliability and successful data forwarding. This model is implemented, and its performances are evaluated by network simulator-NS-3. From the simulation results, the REWQ mechanism minimizes the average deliverance time and improves the residual energy. © 2024 IEEE.

关键词： Water quality

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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2025 International Conference on multi-Agent Systems for Collaborative Intelligence, ICmSCI 2025

作者： muthukumar, D. Surendiran Chowdary, Bommineni Sreekar Kumar, D. Senthil Jagadeesan, P. mythili, B. Swapna, B. Kalasalingam Academy of Research and Education Department of computer science and Engineering Tamilnadu Srivilliputhur India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Department of Computer Science and Engineering Tamil Nadu Chennai India Dr.M.G.R Educational and Research Institute Department of Electronics and communication engineering Chennai India

ISBN: (纸本)9798331509828

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management. © 2025 IEEE.

关键词： Convolutional neural networks

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Haemoglobin measurement from Eye Anterior Ciliary Arteries through Borescope Camera

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computer Systems science & engineering 2023年第2期44卷 1763-1774页

作者： mohamed Abbas Ahamed Farook S.Rukmanidevi N.R.Shanker Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering CollegeChennai600062India Department of Electronics and Communication Engineering R.M.D Engineering CollegeChennai601206India Department of Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringChennai600055India

Nowadays,smartphones are used as self-health monitoring devices for ***-health monitoring devices help clinicians with big data for accurate diagnosis and guidance for treatment through repetitive *** measurement of haemoglobin requires for pregnant women,pediatric,pulmonary hypertension and obstetric *** haemoglobin measurement through conjunctiva leads to inaccurate *** inaccuracy is due to a decrease in the density of goblet cells and acinar units in meibomian glands in the human eye as age ***,conjunctivitis is a disease in the eye due to inﬂammation or infection at the *** is in the form of lines in the eyelid and covers the white part of the ***,small blood vessels in eye regions of conjunctiva inﬂammations are not visible to the human eye or standard *** paper proposes smartphone-based hae-moglobin(SBH)measurement through a borescope camera from anterior ciliary arteries of the eye for the above *** proposed SBH method acquires images from the anterior ciliary arteries region of the eye through a smartphone attached with a high megapixel borescope *** anterior ciliary arteries are projected through transverse dyadic wavelet transform(TDyWT)and applied with delta segmentation to obtain blood cells from the ciliary arteries of the ***,the Gaussian regression algorithm measures haemoglobin(Hb)with more accuracy based on the person,eye arteries,red pixel statistical parameters obtained from the left and right eye,age,and ***,the experimen-tal result of the proposed SBH method has an accuracy of 96%in haemoglobin measurement.

关键词： Hemoglobin measurement borescope camera smartphone anterior ciliary arteries region

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Recent Recommender Systems and Analysis of the machine Learning Public Instances

Recent Recommender Systems and Analysis of the Machine Learn...

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International Conference on Innovative Sustainable Computational technologies (CISCT)

作者： Raja marappan Saraswatikaniga R School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai M.Tech Computer Science and Engineering (Business Analytics) School of Computer Science and Engineering Vellore Institute of Technology Chennai

Recommendation systems have become increasingly important in various online applications. One of the significant types of research in machine learning (mL) is recommendation systems. A recommender system, alternatively referred to as a recommendation system, is an information filtering system that supports users in identifying their “rating” or “preference” for an object and makes predictions based on that rating or preference. This research analyzes current recommendation systems applied in mL public instances to explore knowledge discovery. The suggestions systems are developed for musical, online dating, and restaurant applications. The recommendation system serves as a tool to assist users in discovering what they are interested in by providing them with appropriate ideas. To provide personalized recommendations to users, primarily employ collaborative, content-based, session-based, demographic, and hybrid filtering. Classification, clustering, and association rule discovery are the critical data mining and mL techniques most widely employed in recommendation systems. In general, the accuracy of the recent models lies at (75%, 99 % ) and the error rate occurs at (5%, 25 % ) for the mL public instances.

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A Simplified Policy modification Framework By External provider For Human medical Record Sharing

A Simplified Policy Modification Framework By External provi...

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2023 IEEE International Conference on Research methodologies in Knowledge management, Artificial Intelligence and Telecommunication engineering, RmKmATE 2023

作者： Pandi, C. Siva Rama Lingham, N. Anish, T.P. Aruna, R. Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi India Computer Science and Engineering R.M.K College of Engineering and Technology India

ISBN: (纸本)9798350305708

The great flexibility and accessibility of cloud computing make it a good fit for storing and managing Personal Health Records (PHR), which are often large and complex datasets. Personal health data, on the other hand, include incredibly sensitive and private information, which raises serious security and privacy concerns. There should also be a flexible and secure access plan for PHR owners' outsourced data. Although Some business cloud platforms offer symmetric or public key encryption as an add-on feature, these conventional encryption techniques are not well-suited for data outsourcing due to the high overhead of key management and maintenance. We create and put into practise a secure, granular access control system with minimal policy modifications for outsourced PHRs. On top of AES and Cypher Text encryption, our suggested method is constructed. In order to provide comprehensive policy change tracing, a policy versioning system is also presented. In the conclusion, we evaluated the effectiveness of the suggested scheme by analysing its performance. © 2023 IEEE.

关键词： access control AES performance evaluation PHR policy update policy versioning proxy re-encryption

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machine Learning for Forecasting Depression and Anxiety in University Students 1

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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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Parametrized Optimization Based on an Investigation of musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop music information retrieval Optimization Spark

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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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