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Social Media Addiction: Analysis on Impact of Self-esteem and Recommending Methods to Reduce Addiction 1st

Social Media Addiction: Analysis on Impact of Self-esteem an...

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1st International Conference on Advancements in Smart Computing and Information Security, ASCIS 2022

作者： Ramani, Zarana diwanji, Hiteishi L.D. College of Engineering Ahmedabad India

ISBN: (纸本)9783031230912

In today’s world, everything moves towards virtual from the real world especially business, meetings, shopping, and connections to people. As technology makes life easier, everyone is slowly moving towards it. A platform that allows people to connect with other people all over the world is known as social media. Social media has a wide variety of features that make people stay on that platform. The use of social media has many benefits but excessive use of social media can degrade mental health, and physical health and can affect daily-functional life. Also, human mental health is closely connected to self-esteem. In this research, we have identified the impact of social media addiction on self-esteem and the impact of different variables such as notification response, age, gender, etc. on self-esteem and social media addiction. After analyzing the impact, we have suggested methods to reduce social media addiction based on all groups of people and based on similar social media addiction level groups of people using a collaborative filtering algorithm. As a result, we have found that there is a significant difference in self-esteem based on notification response, and a significant difference in social media addiction based on social media daily usage but there is no significant difference in social media addiction and self-esteem based on gender. Recommending methods to reduce social media addiction based on a similar group of people perform a bit better than all groups of people. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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Real-time Health Monitoring of Patients in Emergencies Through Machine learning And IoT Integrated With Edge Computing 14

Real-time Health Monitoring of Patients in Emergencies Throu...

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14th International Conference on Cloud Computing, data Science and engineering, Confluence 2024

作者： Mishra, Amit Kumar Sharma, Rahul Singh, Jagendra Singh, Prabhishek diwakar, Manoj Tiwari, Mohit Graphic Era Hill University Department of Computer Science & Engineering Dehradun India D. Y. Patil International University Pune India School of Computer Science Engineering and Technology Bennett University Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bharati Vidyapeeth's College of Engineering Dept. of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350344837

With employing the Internet of Things (IoT) sensors and modern-day machine learning algorithms, this study presents a modern inquiry at the intersection of healthcare and era. This have a look at studies the capability of long ShortTerm Memory (lSTM) and Artificial Neural Network (ANN) fashions in forecasting affected person fitness consequences by utilising the exploitation of sensor facts inclusive of temperature, blood strain, ECG, EEG, and pulse price. The study extensively examines the models' performance utilising a broad variety of performance benchmarks. Results demonstrate that once compared to the lSTM model, the ANN version performs better in terms of prediction accuracy, precision, do not forget, and Fl score. This enhanced accuracy indicates how effectively the ANN model can spot challenging patterns in the dataset. The predicted fitness possibilities for 15 human individuals also are proved in a result table, reaffirming the ANN models constant advantage in prediction. This look at proactive patient care, more intelligent treatment regimens, and improved overall patient outcomes is driving the business and is a significant step towards personalised, data-driven healthcare solutions. © 2024 IEEE.

关键词： Forecasting

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Early Prediction of Chronic Obstructive Pulmonary disease: A deep Transfer learning Approach 2

Early Prediction of Chronic Obstructive Pulmonary Disease: A...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Rukumani Khandhan, C. Gothai, E. Arun, G. Bharathi Priya, R. Kongu Engineering College Department of Computer Science & Engineering Perundurai Erode638060 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Pg Chennai600062 India Kongu Engineering College Department of Computer Technology -PG Tamil Nadu Perundurai638060 India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368413

Chronic Obstructive Pulmonary disease (COPd) is a prevalent, crippling lung ailment that often progresses undetected until its advanced stages, leading to significant morbidity and mortality. Early prediction and interference are crucial to improving patient outcomes. Here, a novel approach for the early prediction of COPd using deep transfer learning is proposed. Three cutting edge models from deep transfer learning, namely Inception, ResNet, and VGGNet, are trained to extract and classify features from audio recordings of breathing sounds. These models are fine-tuned on a dataset consisting of sounds from both healthy individuals and those with COPd, along with six other lung disorders. The extracted features are used to predict the presence of COPd, enabling early detection and intervention. The outcome of the proposed methodology demonstrates the efficiency of the model's capability to predict COPd, with the deep transfer learning models achieving high precision in distinguishing COPd patients from non-COPd individuals. This approach shows the potential for cost-effective and early prediction of COPd, facilitating timely interventions and improved management of the disease. © 2024 IEEE.

关键词： lung cancer

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IoT-Enabled Secure Energy Management System in Smart Meters with CRFN: A Novel Approach to Billing and Theft Prevention

IoT-Enabled Secure Energy Management System in Smart Meters ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Suganya, R. Punitha, S. Subalakshmi, N. Kavitha, K. Jeyakarthic, M. Raak College of Engineering and Technology Department of Computer Science and Engineering Puducherry India D. G. Govt. Arts College for Women Department of Computer Science Tamilnadu Mayiladuthurai India Annamalai University Department of Computer and Information Science Tamilnadu Chidambaram India Annamalai University Department of Electrical & Electronics Engineering Chidambaram India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

In response to the challenges posed by missed electricity bill payments and unauthorized power consumption, this study introduces enhancements to existing smart energy meter systems. The proposed improvements include the integration of advanced security features and automation capabilities, such as refined theft control mechanisms and optimized communication protocols between energy providers and consumers. leveraging IoT technology, the system enables real-time energy monitoring, automatic billing, and timely payment reminders via SMS. Additionally, innovative theft prevention measures, including automated power disconnection for overdue accounts and theft detection systems, bolster security and reduce losses. The integration of the Convolutive Recurrent Fusion Network (CRFN) algorithm significantly enhances the system's theft detection and prevention capabilities. The CRFN detects anomalies in power consumption that may indicate theft, such as sudden spikes or drops in usage, through advanced pattern recognition. Upon detecting potential theft, the system can trigger automated responses like alerts or power disconnection, reducing the need for manual intervention. The incorporation of a magnetic reed switch further enhances efficiency by automatically controlling power access for consumers who have not paid their bills by the due date. By addressing these challenges and integrating advancements, the proposed framework aims to create a more efficient, secure, and sustainable energy management solution, ensuring accurate billing, preventing unauthorized usage, and promoting environmental responsibility. © 2024 IEEE.

关键词： Reed relays

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Automated semantic segmentation and diagnosis of Coconut Tree Infection Using drone Imagery and Machine learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vasumathy, M. devakanth, J. Jude Moses Anto Thirugnanam, Mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GdP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. Major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 IEEE.

关键词： drones

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IoT based deep learning approach for online fault diagnosis against cyber attacks

IoT based deep learning approach for online fault diagnosis ...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Felix, A. Yovan Sharmila, V. devi, S. Nandhini deena, S. Yadav, Ajay Singh Jeyalakshmi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering R.M.D. Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering VSB Engineering College Tamil Nadu Karur India Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Mathematics SRM Institute of Science and Technology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad India Department of Physics PSNA College of Engineering and Technology Tamil Nadu Dindigul India

ISBN: (纸本)9781032669663

The Internet of Things (IoT) connects systems, software, cloud computing, activities and could provide new access points for cyber-attacks. Virus attacks and unauthorized downloading are the two main risks to IoT security at the moment. These dangers run the risk of obtaining private information, damaging their reputation and their finances. A frame is employed in this paper’s deep learning to identify threat avoidance in IoT. The cyber security detection systems following database has to be built, after which the benchmark variables are chosen and assessed in order to establish a cyber-security alert system in a large data set. The deep learning strategy to identifying threat avoidance was described for the simulation of this scenario. From the Mailing database, malware samples have been gathered for analysis. The research results demonstrate that the proposed method for assessing risks to cyber security in IoT provides highest accuracy to current approaches. Because of this, deep Convolution Neural Network (dCNN) is utilized in IoT to detect attack avoidance. © 2024 The Author(s).

关键词： Internet of things

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The λNF Problem 2nd

The λNF Problem

引用

2nd International Conference on Cybersecurity and Evolutionary data engineering, ICCEdE 2022

作者： dutta, Anurag Munjal, deepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The dutch National Flag problem—dNF Problem, a Computational Problem proposed by the dutch computer Scientist—Edsger Wybe dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd 2023.

关键词： Computational complexity dnf sort Partitioning Sorting algorithms

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Curating Stopwords in Marathi - A TF-IdF Approach 9

Curating Stopwords in Marathi - A TF-IDF Approach

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Chavan, Rohan Madle, Vishal Patil, Gaurav Joshi, Raviraj Walchand College of Engineering Sangli Computer Science And Engineering Maharashtra Sangli India L3Cube Labs Pune Maharashtra Pune India Indian Institue of Technology Madras India

ISBN: (纸本)9798350394474

Stopwords are commonly used words in a language that are often considered to be of little value in determining the meaning or significance of a document. These words occur frequently in most texts and don't provide much useful information for tasks like sentiment analysis and text classification. English, which is a high-resource language, takes advantage of the availability of stopwords, whereas low-resource Indian languages like Marathi are very limited, standardized, and can be used in available packages, but the number of available words in those packages is low. Our work targets the curation of stopwords in the Marathi language using the MahaCorpus, with 24.8 million sentences. We make use of the TF-IdF approach coupled with human evaluation to curate a strong stopword list of 400 words. We apply the stop word removal to the text classification task and show its efficacy. The work also presents a simple recipe for stopword curation in a low-resource language. The stopwords are integrated into the mahaNlP library and publicly available on https://***/l3cube-pune/MarathiNlP. © 2024 IEEE.

关键词： Sentiment analysis

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Experimental Evaluation of a Secured Phrase Searching Model Using Cipher Principles Over Cloud Computing Platform

Experimental Evaluation of a Secured Phrase Searching Model ...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Geetha, R. Parameshwari, M. Kanniga Praveena, V. Venkatesan, T. Sindhu, M. Bhuvana, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Information Technology Tamil Nadu Chennai India Tamil Nadu Chennai India R. M. D. Engineering College Department of Mathematics Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Sriram Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350375237

For many cloud-based IoT device learning applications, including sensible clinical data analytics, phrase search - which allows retrieval of files containing a given term - plays a crucial role. In order to prevent sensitive information from being exposed by third-party providers, data owners typically encrypt files (such as medical records) before sending them to the cloud. Having said that, this does make the search operation quite challenging and current searchable encryption systems for multiple keyword searches can't do word searches because they can't determine the location connection of multiple key phrases in a query word using encrypted data stored on the cloud server side. This study presents a model called MClPS, which stands for 'Modified Cyber law for Phrase Searching.' Its purpose is to identify specific phrases in a cloud environment. To test how well the model works, it is cross-validated with the traditional BET. The safety of information in the cloud has long been an important concern for cloud service providers and their clients, despite the fact that there are several elements that could affect data security during searches. The reason behind this is the presence of dangers posed by both external data sources and internal partners, namely, their employees. Therefore, due to its massive structure, maintaining this security perfect is an ongoing headache and difficult effort in the cloud. In most cases, this is achieved by encrypting data at all times and taking extra precautions to ensure that data is not compromised during the audition or any other internal exercises. For safe phrase search in cloud IoT, this study suggested the MClPS model and the BET encryption technique. The data integration, recovery, indexing, sliding, encryption, decryption, and transfer processes are all encompassed in the suggested works. This proposed study introduces a new approach to encrypted searching that allows encrypted phrase searches, and the MClPS ad BET proced

关键词： Cryptography

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An Analysis of Face based eCrime Identification using First Information Report 3

An Analysis of Face based eCrime Identification using First ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Pandiaraja, P. Vanarasan, S. dharani, S. dharshini, R. Harani, S. Selvarathi, C. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai600 062 India Sri Sairam College of Engineering Department of Artificial Intelligence and Machine Learning Banglore562 106 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur639 113 India

ISBN: (纸本)9798331509675

Face based e-crime identification initiative combines cutting-edge technologies to detect criminals in video footage, allowing for quick and effective online FIR (First Information Report) reporting. Furthermore, it uses blockchain technology to securely store and manage sensitive data, as well as to ensure the integrity of evidence and to facilitate robust facial recognition using the Inception V3 algorithm. The proliferation of electronic crimes (eCrimes) in today's digital age poses a huge challenge to law enforcement organizations worldwide. In the face of continuously shifting criminal strategies, traditional means of identifying and reporting offenders frequently fall short. The fundamental functionality of the technology is around identifying offenders using video footage. It makes use of the potent Inception V3 algorithm, a cutting-edge deep learning model noted for its efficient image recognition skills. This algorithm analyses video frames, detects and matches facial traits, and identifies suspects. This not only improves the accuracy of criminal identification but also speeds up the investigation. In addition to identifying criminals, the streamlines the FIR reporting process. Victims and witnesses can now conveniently file FIRs online, eliminating the need for physical visits to police stations. The integration of digital reporting platforms enhances the efficiency of law enforcement agencies, making the process more accessible to the public. Blockchain technology is used to provide data security and immutability. A blockchain-based database stores all data relevant to criminal identification and FIR reporting. The combination of improved facial recognition, online reporting, and secure blockchain data management promises to make identifying and reporting criminals more efficient and secure. © 2025 IEEE.

关键词： deep learning

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