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A Review on the Methods of Targeted Drug Delivery Using Nanotechnology

A Review on the Methods of Targeted Drug Delivery Using Nano...

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engineering and technology (PICET), Parul International Conference on

作者： Mohan Warbhe Prateek Verma Swapnil Gundewar Palash Gourshettiwar Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institude of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The use of nanotechnology for targeted drug delivery is a rapidly growing field. Nanotechnology, with its manipulation of materials at the nanoscale, offers novel solutions across various fields, prominently in medicine. Nanotechnology enables precise drug administration to specific sites in targeted drug delivery, ensuring optimal efficacy while minimizing side effects. This approach encompasses passive and active targeting strategies, leveraging biological mechanisms and ligand-mediated interactions for enhanced drug delivery. The current study reviews the techniques of targeted drug delivery using nanotechnology, focusing on various methods of targeted drug delivery, their applications, and strategies for using nanotechnology. The key processes include drug loading, release, and overcoming biological barriers. Nanotechnology also finds applications in imaging, therapeutics, and drug development, promising personalized and precise medical interventions. Although several challenges, such as scalability, drug loading, and biological barriers, persist, the future scope remains promising, with personalized medicine, precision targeting, biomimetic nanoparticles, and advanced imaging integration poised to revolutionize healthcare.

关键词： Drugs Nanoparticles Targeted drug delivery Reviews Precision medicine Biomimetics Loading Medical services Nanotechnology Biomedical imaging

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Integrated IoT-Based Smart Home Security System: Real-Time Hazard Detection and Alerts Using Multi-Sensor Fusion

Integrated IoT-Based Smart Home Security System: Real-Time H...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Sushma, A.M.S.V. Aswini Priyanka, A. Lakshmi, A.N.G. Khatri, Narendra Sharma, Harish S R K R Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Bhimavaram India S R K R Engineering College Department of Computer Science and Design Engineering Andhra Pradesh Bhimavaram India Sir C.R. Reddy College Engineering Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India Manipal Acacdemy of Higher Education Department of Mechatronics Manipal Institute of Technology Manipal India Manipal University Jaipur School of Computer Science and Engineering Department of Artificial Intelligence and Machine Learning Jaipur India

ISBN: (纸本)9798350390049

This paper presents an innovative IoT-based smart home security system that integrates multiple sensors to detect various hazards and provide real-time alerts. Unlike traditional single-function systems, this solution combines motion detection via a PIR sensor, gas leak detection using an MQ-2 sensor, and fire detection through an IR flame sensor into a single, affordable package. The Arduino UNO serves as the central controller, interfaced with a GSM module for instantaneous SMS notifications and supported by an on-site buzzer alarm. Extensive testing in diverse environments demonstrates the system's exceptional performance, detecting intrusions in under 2 seconds and reporting threats with approximately 89% accuracy, while minimizing false alarms. The integration of smart power management techniques reduces energy consumption compared to conventional systems. This approach not only enhances home security but also offers a scalable and easily deployable solution for both residential and commercial settings. This work addresses the urgent need for an affordable, multi-functional smart home security system and provides a robust framework for future IoT-driven safety solutions. © 2024 IEEE.

关键词： Smart homes

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Innovative Healthcare Advancements: Harnessing artificial and Human intelligence for Bionic Solutions

Innovative Healthcare Advancements: Harnessing Artificial an...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Sapkal, Samruddhi Jadhav, Sulaxan Mallikarjun, P. Shamim, Rejuwan Ul Islam, Atowar Bamane, Kalyan D y Patil International University Artificial Intelligence and Machine Learning Pune Akurdi411044 India D y Patil International University Pune Akurdi411044 India Malla Reddy Engineering College Jntuh Department of Electrical and Electronic Engineering Hyderabad India Maharishi University of Information Technology School of Data Science Noida India University of Science and Technology Meghalaya Department of Computer Science and Electronics Ri-Bhoi Techno city Kiling Road Meghalaya Baridua793101 India D y Patil College of Engineering Department of Information Technology Pune Akurdi India

ISBN: (纸本)9798350373783

According to initial data, individuals who have been diagnosed with type 2 diabetes (T2DM) appear to be at a more chances of evolving breast cancer compared to those who have not received a T2DM diagnosis. The primary goal of the research was to estimate the efficacy of three dissimilar methods in forecasting the probability of breast cancer in T2DM patients with diverse attributes. To achieve this objective, a danger expectation model was created for breast cancer in individuals with T2DM using the primary *** ensure a comprehensive analysis, we also gathered information on potential factors that may predict the growth of breast cancer. As the population sample size was restricted, we utilized Synthetic Minority Oversampling technology to amplify the quantity of accessible data. Random assignment of data points to training or test sets was conducted at a ratio of roughly 39 to 1. Three distinct models, specifically artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), were assessed for effectiveness using a range of performance criteria, including as F1 score, area under the receiver operation characteristic curves, recalled, and correctness. (AUC). AUC values for these models were as follows: LR had an AUC of 0.834, ANN had an AUC of 0.865, and RF had an AUC of 0.959, with RF having the greatest AUC. According to our study, the Random Forest model outperformed the LR and ANN models in correctly estimating the incidence of breast cancer in people with T2DM. © 2024 IEEE.

关键词： Diseases

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Churn Prediction in Financial Risk Management using Deep learning Techniques 2

Churn Prediction in Financial Risk Management using Deep Lea...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Kumar, V Sunil Sowmyashree, S. Pareek, Piyush Kumar Khanna, Ashish Costa De Albuquerque, Victor Hugo Gupta, Deepak Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Computer Science & Engineering East West Institute of Technology Bengaluru India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Cearά Fortaleza Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Federal University of Cearά Department of Teleinformatics Engineering Brazil

ISBN: (纸本)9781665493963

It is essential to develop an efficient Client Relationship Model in order to make use of the estimations about customer turnover (CRM). Finding and breaking down the facts surrounding the business information understanding is necessary for client produce prediction, which should be made possible effectively by altering the strategies used for business insight. The tools that are provided by company insight allow for the historical, current, and prospective perspectives of business activity to be anticipated and dissected. It is possible to deal with it in an efficient way by providing the information mining approaches that choose the data from the created informative index that is the most helpful. Fundamental leadership should be possible to achieve in a fruitful way if one has this information. The actions that consumers have taken in the recent past are going to be analyzed, and an accurate forecast is going to be formed about those customers who are likely going to get unhappy in the near future based on the results of that prediction. The problem is that deciding on a certain period to investigate the client's activities and also establishing on a specific day and age for each of the clients won't be appropriate6. This is due to the fact that the problem is caused by the fact that deciding on a certain period to investigate the client's activities will take too long. The challenge lies just in this aspect. How to decide on a certain amount of time to spend researching the activities of the customer. For instance, when planning a model, 100 customers are used, 70 of those customers are considered 'active,' which indicates that they are still conducting business with the organisation, and the remaining 30 customers are considered 'agitate,' which indicates that they have severed their ties with the organisation. The primary purpose of the work that has been suggested is to carry out exploratory research and management as a multicriteria basic leadership issue, a

关键词： Forecasting

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AI-Powered Advertisement Design: A PIL-Based Approach for Quality and Performance Analysis

AI-Powered Advertisement Design: A PIL-Based Approach for Qu...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Harshitha Chilupuri G. Ramesh J. Praveen Venkataramaiah Gude Dept. of Artificial Intelligence and Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electrical and Electronics Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India GP Technologies LLC U.S.A.

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

The paper presents the development of a Data Science solution that applies generative AI models in order to create high-quality images to be used in visual marketing and advertising. Using the Python library PIL (Pillow) along with machine learning frameworks, the solution combines predefined product images with the selected backgrounds, enhancing image synthesis techniques for more appealing and contextually relevant visuals. This leads to improved efficiency and effectiveness in the creation of content by automatically placing product images onto the background images used in marketing campaigns. The approach demonstrated a 98.06% SSIM (Structural similarity Index) score, indicating significant visual quality improvement. Initial results suggest a 15% enhancement in visual appeal compared to traditional image creation methods.

关键词： Visualization Image transformation Generative AI Data science Libraries Performance analysis Indexes Advertising Optimization Python

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AES Algorithm for the Next Generation of 5G Network Encryption Standards 2

AES Algorithm for the Next Generation of 5G Network Encrypti...

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Manojkumar, T. Tamilarasi, K. Mahalakshmi, S. Vinayagam, P. Mahanthesha, U. Mohanraj, S. Karpagam Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Coimbatore India Excel Engineering College Department of Electronics and Communication Engineering Tamilnadu Namakkal India R.M.K. Engineering College Department of Computer Science and Design Tamilnadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Bnm Institute of Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350315905

The Advanced Encryption Standard (AES) symmetric key encryption algorithm is the industry standard for 5G communication encryption. technology advancements nowadays are mostly aimed at improving the security, speed, and efficiency of processors. The main expansion phase is made easier by the study's pipeline structure. This method simplifies the process of creating subkeys in AES encryption for use in 5G communication networks. There is a 37% increase in throughput between the simulated and implemented versions of the original design on FPGA Virtex4 XC4VLX200, and this difference may be attributed to the reduction in propagation latency while producing subkeys. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Complex Pattern Recognition using Feedback Algebraic Activation Function 2

Complex Pattern Recognition using Feedback Algebraic Activat...

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2nd International Conference on Communication, Computing and Industry 4.0, C2I4 2021

作者： Dhivya, P. Bazilabanu, A. Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Tamilnadu Erode India

ISBN: (纸本)9781665420136

Various researches on the Wisconsin breast cancer dataset in the prediction of benign and malignant were proposed in this work. The various classification models were used in the literature survey in which logistic regression give the better accuracy of 95.41%. But in machine learning model, the linear relationship between the features may lead to over fitting in that model. The neural network is a fully connected layer to find and understand the nonlinear combinations of the features in the dataset. To reduce the over fitting and improves the accuracy of the model, the Neural Network with Feedback Algebraic Activation (FAA) function is introduced and applied to the hidden layer in the network. In this, the error will be added as a feedback to the network and uses the keras callbacks and keras tuner for selecting the optimal parameters and produces the better results with the accuracy of 95.91% compared with all other activation functions in the neural network. © 2021 IEEE.

关键词： Algebra

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Impacts of Fake Reviews on Dietary Supplements and Healthcare Products in Social Media

Impacts of Fake Reviews on Dietary Supplements and Healthcar...

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International Conference on Computing and Networking technology (ICCNT)

作者： Nithin M Kannal N Asmathunnisa Jagadish S Kallimani Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bangalore India Visvesvaraya Technological University Belagavi Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In today’s digital landscape, the prevalence of misinformation is a pressing concern, particularly on social media platforms where approximately $42.8 \%$ of shared content is identified as inaccurate and fake, as highlighted by a 2019 study from Loughborough University’s Online Civic Culture Centre. This phenomenon extends beyond mere inconvenience, with false information on political issues and product reviews posing significant threats to societal trust, business integrity, and political discourse. Furthermore, “The State of Fake Reviews - Statistics and Trends” by InvespCRO (2023) underscores a concerning trend: the steady rise in fake reviews from 2019 to 2023, indicative of a persistent challenge in maintaining online authenticity. This article delves into the intriguing world of online reviews, focusing specifically on the impact of falsified reviews within the realm of supplements and health products. With the power to sway consumer opinions and shape company reputations, false reviews wield considerable influence, often distorting market perceptions and consumer choices. By immersing ourselves in a hypothetical scenario where a product garners an alluring yet deceitful review, we unravel the ripple effects across various sectors, illuminating the far-reaching consequences of deceptive practices.

关键词： Reviews Social networking (online) Shape Companies Pressing Market research Fake news Public healthcare Business Gravity

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Estimation of Response Time with Neural Networks in Cloud Computing System

Estimation of Response Time with Neural Networks in Cloud Co...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Rajesh More Sanjna Suresh Sathish Kumar Veerappampalayam Easwaramoorthy Haider Ali Rizvi Kannan Kunnathully Sahil K. Gupta Departmen of AIML Cloudwerx Wordwide Hyderabad Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore School of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Department of Artificial Intelligence and Machine Learning Yogananda School of Artificial Intelligence Shoolini University Department of Research Veltech University Chennai Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Resource allotment, responsiveness, connection, research of unexplored assets, related resource modeling, and asset scheduling are just a few of the numerous factors and concerns that need to be considered to enhance cloud computing efficiency. The functionality of the cloud has a significant impact on the propagation of cloud architecture. Consequently, it is important to determine the components that influence cloud function to analyze it. Response duration is the primary aspect that significantly affects cloud computation functioning, according to the research. The response duration assistance is among the numerous different variables that may be used to organize resource applications. artificial neural networks (ANNs) were employed as a technique for evaluating the parameter of pursuit, and a fork-join framework or an architecture with demand partitioning was selected as the queuing paradigm. As a result, this study suggests a load-managing method depending on cloud demand response duration using the ARIMA Estimation of Response Duration Method. The fundamental concept is to employ ARIMA methods to estimate the future response duration, providing a more efficient method of allocating resources with limited values. The results of the investigation demonstrated that the estimations were more precise compared to those that had been recently established. The results of the study indicate that estimation is an outstanding path for load balance and has possibility and value for load balance with displayed response duration. Additionally, the suggested method makes it feasible to acquire estimations for a wider range of cloud structure functioning signs compared to earlier feasible and also to extend the evaluation of the cloud platform to the instance of a structure with a non-Poisson data flow and non-exponential assistance duration.

关键词： Computer science Cloud computing Processor scheduling Estimation Computer architecture artificial neural networks Load management Scheduling Demand response Time factors

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An Intelligent Software Tool for Audio Signal-based Hinglish to English Language Translation

An Intelligent Software Tool for Audio Signal-based Hinglish...

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IEEE Punecon

作者： Aditi Vikrant Dhavale Amit Vikrant Dhavale Sunita Vikrant Dhavale Department of Artificial Intelligence & Machine Learning Symbiosis Institute of Technology Pune India Department of Mechanical Engineering Indian Institute of Technology Indore India SoCE & MS Defence Institute of Advanced Technology Pune India

Most of the current Indian generation speaks/writes using Hinglish on various social media platforms. It is difficult to understand and interpret Hinglish using existing computational linguistic tools mainly based on English. Hence, there is a need to provide automated Hinglish to English Language Translation tools to integrate with existing NLP-based applications like hate speech detection. Currently, such complex language translations are manually intensive and require domain experts. Also, there is a scarcity of standard datasets in this domain for carrying out helpful research. In this research, we propose an intelligent AI-based translator for translating audio data signals from Hinglish to English, which can be integrated into various NLP-based applications in the Indian Context. We also created a large-scale customized annotated Hinglish to English dataset containing 27135 entries of size 4.121MB. After extensive experimentation, we received the highest BLEU (Bi-Lingual Evaluation Understudy) score of 92.20 on our customized Hinglish to English dataset, while on the original PHINC dataset, we found a BLEU score of 70 approx. We also developed a Python TKinter-based user-friendly GUI application that takes audio signal/voice as input and provides English-translated text as output.

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