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7th international Conference on trends in Electronics and informatics, iCOEi 2023

作者： Balde, Govind Hanmantrao Ala Walid, Md. Abul srinivasan, P. Yella, Venkata Rajesh soumya, M. Rastogi, Ravi N.T.V.S's G. T. Patil Arts Commerce and Science College Research and P.G. Department of Zoology M.S. Nandurbar425412 India Department of Computer Science and Engineering Khulna9203 Bangladesh Sona College of Technology Department of Ece Tamilnadu Salem India Koneru Lakshmaiah Education Foundation Department of Biotechnology Andhra Pradesh Vaddeswaram India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Nielit Electronics Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350397284

Cell density prediction can be defined as the process of predicting the number of cells in a given quantity of a culture or cell suspension. it is considered a common practice in cell biology since cell density had a significant impact on cell behavior and can be utilized for monitoring the health and growth of cell culture. Precise prediction of cell density was significant for a range of applications in cell biology., which includes bioprocessing, cell-based assays, and cell culture. therefore, this article develops a novel Cell Density Prediction design using Optimal Deep Learning with salp swarm Algorithm (CDP-ODLssA) technique. the presented CDP-ODLssA technique predicts the cell densities accurately on the images of cell suspensions or cultures. to do so, the presented CDP-ODLssA technique employs Long short term Memory-Autoencoder (LstM-AE) model for prediction of cell densities. in addition, the hyperparameter tuning of the LstM-AE model takes place by the use of salp swarm Algorithm (ssA). For experimental validation of the CDP-ODLssA technique, a wide range of simulations was taken place. the obtained values highlighted the superiority of the CDP-ODLssA technique compared to other approaches. © 2023 iEEE.

关键词： Long short-term memory

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An innovative Method for Paddy Yield Prediction Based on DCNN-ELM Approach 2

An Innovative Method for Paddy Yield Prediction Based on DCN...

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2nd international Conference on intelligent Data Communication technologies and internet of things, iDCiot 2024

作者： Gandhi, Mohd. Asif singh, Lakhwinder Prabakaran, V. Bhupal Raj, G. sowmiya Patil, Harshal Anjuman-I-Islam's Kalsekar Technical Campus Affiliated to University of Mumbai Department of Mechanical Engineering Panvel India Lovely Professional University Department of Geography Phagwara India SNS College of Technology Department of Management Studies Tamil Nadu Coimbatore India School Of Agriculture SR University Warangal India Prince Shri Venkateshwara Padmavathy Engineering College Department of CSE Chennai India Computer Science And Engineering Maharashtra Pune India

ISBN: (纸本)9798350327533

the majority of a country's population relies on agriculture for their livelihood. Water shortage, uncertainty in market prices due to supply and demand, erratic weather, and inaccurate crop predictions are just some of the new realities that farmers must adjust to. Environmental conditions, management tactics, crop genotype, and yield all interact in complex ways, making crop yield prediction difficult. this is especially true for paddy cultivation. Researchers utilize to forecasting the paddy yield through statistical methods, yet they failed to obtain greater precision due to several causes. Within the suggested methods, preprocessing, feature selection, and model training are all included. they employ adaptive data cleaning and denoising auto encoder in data preprocessing. All of the missing values in the input dataset are filled in using the pre-processing technique. they employ feature selection the CFs procedure uses statistical measurements as evaluation criteria to determine the quality of feature subsets. DCNN-ELM trains the models after the attributes are gathered. Compared to the DCNN and ELM techniques, the suggested strategy yields significantly better results. At 96.33 percent, this technique has an incredibly high probability of success. © 2024 iEEE.

关键词： Denoising Auto Encoder (DAE) Extreme Learning Machine (ELM) Paddy Yield Prediction

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intelligent inhalation therapy for Cystic Fibrosis Using iot and Machine Learning solutions 4

Intelligent Inhalation Therapy for Cystic Fibrosis Using IoT...

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4th international Conference on sustainable Expert systems, iCsEs 2024

作者： Ranganathan, Chitra sabapathy Arul Prasath, A. Nandhini, R. shanmugam, Kannan Velmurugan, s. srinivasan, s. Mphasis Corporation Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing Science and Engineering Vit Bhopal University Department of Gaming Technology Madhya Pradesh Bhopal India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331540364

Optimal patients result in the long-term management of cystic fibrosis (CF) and need personalized treatment strategies due to the hereditary disorder's impact on respiratory problems. One potential approach to individualized CF treatment is intelligent inhalation therapy systems that combine machine learning (ML) algorithms with internet of things (iot) sensors. to provide CF patients with a predictive modeling framework that can personalize their inhalation treatment using the Random Forest algorithm. the algorithm is designed to predict individual-level outcomes, including the potential of an exacerbation and the efficacy of therapy, by mining a large and varied dataset that includes patient demographics, lung function measurements, medication adherence records, environmental factors, and reports of symptoms. the Random Forest method was selected due to its capacity to manage diverse data types, detect intricate relationships between variables, and provide insights that can be understood. it shows that the Random Forest model effectively predicts important outcomes for CF care by training, validating, and evaluating it using real-world data from CF patients. Exacerbation risks and patient-specific therapy responses may be better understood by feature significance analysis, allowing for more targeted intervention approaches. the proposed predictive modeling framework will improve the efficiency and effectiveness of inhalation treatment for managing CF. Healthcare practitioners may enhance the management of CF via ML analytics and data collected through the iot. this allows for delivering personalized treatment regimens, optimizing resource allocation, and better patient outcomes. Challenges include ensuring data accuracy, managing extensive datasets, sustaining real-time monitoring, optimizing algorithms, and ensuring system stability. © 2024 iEEE.

关键词： Personalized medicine

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structural,morphological,optical and antibacterial performances of rare earth(sm)-doped ZnO nanorods

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Journal of Rare Earths 2024年第11期42卷 2119-2127,I0005页

作者： A.Ravi J.samuel s.sahaya Jude Dhas s.Usharani turibius simon D.senthil Kumar s.K.Jasmin Vijitha A.sivakumar Raju suresh Kumar C.s.Biju Department of Physics University College of EngineeringKancheepuram631552TamilnaduIndia Department of Physics and Research Centre Malankara Catholic CollegeMariagiri629153TamilnaduIndia Manonmaniam Sundaranar University Abishekapatti627012TirunelveliTamilnaduIndia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha UniversityChennaiTamil Nadu602105India Department of Chemistry S.I.V.E.T CollegeGowrivakkamChennai600073TamilnaduIndia Department of Biotechnology Vivekanandha College of Arts and Science for Woman(Autonomous)Elayampalam637205TiruchengodeNamakkalTamilnaduIndia Department of Physics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyAvadiChennai600062India Department of Physics&Research Centre Muslim Arts CollegeThiruvithancode629174TamilnaduIndia Key Laboratory of High-temperature and High-pressure Study of the Earth's Interior Institute of GeochemistryChinese Academy of SciencesGuiyang550081China Department of Chemistry College of ScienceKing Saud UniversityP.O.Box 2455Riyadh11451Saudi Arabia Department of Physics St.Alphonsa College of Arts and ScienceSoosaipuramKarinkal629157Tamil NaduIndia

the significant rise of ultra-violet(UV) radiation and pathogenic infectious bacteria poses a serious threat to global *** researchers' interests are attracted by novel materials with strong UVblocking ability,antibacterial activity and low toxicity to other *** this case,a simple wet chemical method with different annealing temperatures(400,500,and 600℃) was employed to create highly effective rare earth(sm)-doped ZnO ***(101) plane of wurtzite ZnO shifts towards a lower angle with increasing annealing temperature,according to the X-ray diffraction(XRD) study findings,which additionally establishes the consequence of lattice *** of doublet peaks of sm 3d(sm 3d_(5/2) and sm 3d_(3/2)) in the X-ray photoelectron spectroscopy(XPs) spectrum clearly validates the substitution of sm^(3+) ions in the 500℃-annealed *** 500℃-annealed nanorods exhibit combined performances of the wide band gap,improved UV absorbance,and vivid green luminescent emission(563 nm).Additio nally,the nanorods have favorable UV-blocking execution of 96% for UVA at 360 nm,92% for UVB at 320 nm,and 57% for UVC at 225 nm,which is greater than the majority of ZnO-related materials that have been reported up to this *** doping is also appropriate for improving bacterial inhibition against the two studied strains(Escherichia coli and staphylococcus aureus),in addition to the intriguing features discussed ***,with maximum inhibition zone diameters of 20±0.72 and 18±0.57 mm,respectively,these nanorods exhibit high inhibitory action against *** and *** bacterial *** rare earth-doped material developed during the current experiment,which was annealed at 500℃,could potentially serve as an effective replacement for UV-blocking and antibacterial material,especially for biomedical applications.

关键词： Wet chemical method Rare earth doping Metal oxide UV-shielding Bacterial inhibition

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smart Electric Vehicle (EVs) Charging Network Management Using Bidirectional GRU - AM Approaches 2

Smart Electric Vehicle (EVs) Charging Network Management Usi...

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2nd international Conference on Edge Computing and Applications, iCECAA 2023

作者： Gandhi, Mohd. Asif Priya, K. Charan, Piyush sharma, Ritu Rao, G. Nageswara suganthi, D. Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Maharashtra Panvel India Marudhar Kesari Jain College for Women Department of Computer Science Tamil Nadu Vaniyambadi India Manav Rachna University Department of Electronics and Communication Engineering Haryana Faridabad India Ggits Madhya Pradesh Jabalpur India Lakireddy Bali Reddy College of Engineering Jntuk Kakinada Electrical and Electronics Engineering Department Andhra Pradesh Mylavaram India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Tamilnadu Chennai India

ISBN: (纸本)9798350347579

Electric Vehicles (EVs) are now essential since electrifying transportation has shown to be a game-changer in raising the sustainable and eco-friendly platform in global industry. integrating Electric Vehicle Charging system (EVCs) as a new entity into the power distribution system is one of the most important and challenging concerns. the development of an EVCs network infrastructure is a key step toward the broad adoption of EVs. in order to make informed judgments about transmission, distribution, energy allocation, and charging station placement, the control center or central aggregator must have an accurate forecast of occupancy, consumption, and energy or charging demand. Data analytics and other methods have made it possible to regularly get information from the EVCs for the purposes of archiving and processing all of the data collected. this proposed approach to presents a solution to the aforementioned problems with energy demand forecasting for EVCs networks. three steps make up the proposed method: preprocessing, feature selection, and model performance evaluation. Preprocessing data via normalization, feature selection by K-Means, and ultimately model evaluation via K-means. the proposed model has superior results to the LstM, GRU, and BiGRU - AM models. © 2023 iEEE.

关键词： Data Analytics

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A Multimodal Content Moderation system using Adversarial Machine Learning

A Multimodal Content Moderation System using Adversarial Mac...

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Applied Artificial intelligence and Computing (iCAAiC), international Conference on

作者： A. Barveen M.K. Mohamed Faizal s. Geetha Department of Artificial Intelligence and Data Science M.I.E.T. Engineering College Trichy Tamilnadu Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy Tamilnadu Department of Computer Applications University College of Engineering Trichy Tamilnadu

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. it is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LstM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLstM and CNN have been used to extract contextual features. this study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. the proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. the proposed system uses both text and image data. to improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training.

关键词： training Learning systems Deep learning Visualization technological innovation social networking (online) Feature extraction

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Development of CNN Model to Avoid the Food spoiling Level

Development of CNN Model to Avoid the Food Spoiling Level

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2023 international Conference on New Frontiers in Communication, Automation, Management and security, iCCAMs 2023

作者： Usha, R. selvan, R.senthamil Basi Reddy, A. Chandrakanth, P. Madanapalle Institute of Technology & Science Department of Cse Madanapalle India Nnamacharya Institute of Technology and Sciences Department of Electronics and Communication Engineering Tirupati India Mohan Babu University School of Computing Department of Computer Science and Engineering Tirupati India N.B.K.R.I.S.T Vidyanagar Department of Cse Kota India

ISBN: (纸本)9798350317060

Food grains are sensitive to deterioration owing to precipitation temperature, humidity, and several other variables;thus, researchers are working toward novel approaches to preserve the dietary value of food to improve the shelf lifespan of food. therefore, reliable methods of monitoring food deterioration are necessary to maintain current food safety standards. A prototype has been developed to monitor food quality and manage storage systems within domestic settings. the CNN (Convolutional Neural Network) model was initially utilized for fruit and vegetable classification. the proposed system utilizes actuators and sensors to monitor the emission of gases, humidity, and the temperature of vegetables and fruits to assess the degree of food spoilage. Furthermore, this measure effectively regulates the surrounding conditions and minimizes the occurrence of food deterioration whenever feasible. Customers are notified through text message when the food they ordered is about to deteriorate based on the product's freshness and condition. the used model has a confirmed accuracy of 96%. some types of food could extend their storage life by two days, meaning the experiment succeeded. © 2023 iEEE.

关键词： Neural network models

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A Cloud-Based UV Monitoring system for Remote Real-time UV Exposure tracking 4

A Cloud-Based UV Monitoring System for Remote Real-Time UV E...

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4th international Conference on smart Electronics and Communication, iCOsEC 2023

作者： Dankan Gowda, V. Prasad, V. Nuthan Prasad, K.D.V. Prasad, V.K. satya Mahajan, Yogesh suneetha, sampathirao Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India M S R i T Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Pune India Symbiosis International University Symbiosis Centre for Management and Human Resource Development Maharashtra Pune India Andhra University College of Engineering Andhra University Department of Computer Science and Systems Engineering Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350300888

Due to the widespread nature of sun-related health risks, sophisticated UV monitoring systems are urgently needed. this research study introduces a cloud-based UV monitoring system with the intent of remotely tracking and analyzing UV exposure in real time. the system incorporates a network of UV sensors deployed in various locations, continuously collecting UV radiation data. these sensors are linked to a cloud system, which allows for easy sharing and processing of collected data. As a result, we're working to perfect a cutting-edge device that can monitor vitamin D absorption, measure cumulative UV exposure, and warn of impending danger. Extensive studies have demonstrated the benefits of UV exposure for both general health and protection against the newly discovered coronavirus. However, finding a happy medium is important because too much sun exposure can cause cancer, lupus flares, and cataracts. the proposed model gives users the ability to set their own daily UV exposure limits, taking into account clinically validated thresholds for various skin tones. the developed sensor module is able to measure the UV index with high precision even in bright sunlight because it closely mimics the skin erythema spectrum. © 2023 iEEE.

关键词： Ultraviolet radiation

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Hourly trend Analysis of spatio-temporal Precipitation Extremes Using tRMM and GPM satellite Data on EtCCDi indices for the indian Region

Hourly Trend Analysis of Spatio-temporal Precipitation Extre...

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Communication, Control and intelligent systems (CCis)

作者： Heli Hingrajiya Yuvrajsinh Bodana Khyati Amin Amit thakkar Hiteshri shashtri Department of Computer Science and Engineering C.S.P.I.T CHARUSAT TIX Ecosystems Pvt.Ltd Vadodara India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

india relies heavily on rainfall for agriculture, water management, and climate planning. satellite missions like the tropical Rainfall Measuring Mission (tRMM) and Global Precipitation Measurement (GPM) provide global rainfall data, but they haven’t been fully used for studying daily or hourly rainfall in india. this research looks at how daily data from these satellites can help analyze rainfall patterns over time. it uses methods like the Mann-Kendall test and sen’s slope estimator, compatible with handling satellite data. the study analyzes each hour starting from the 00th to the 21st (00th, 03rd, 06th, 09th, 12th, 15th, 18th, 21st), i.e., daily data split into 3-hour intervals to infer insights into diurnal changes within precipitation patterns. Moreover, through this process, the recent shift of extreme rainfall activities from the Bay of Bengal towards the Arabian sea is also addressed, and a call for action is made through which the government and meteorological organizations can draw meaningful conclusions for early future calamity prediction.

关键词： interpolation Rain satellites stacking Government sea measurements Market research Planning Jamming intelligent systems

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Pattern Mining—FtisPAM Using Hybrid Genetic Algorithm

Studies in Big Data

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studies in Big Data 2021年 89卷 353-369页

作者： Gladence, L. Mary Priya, s. shanmuga sam, A. shane Pushparathi, Gladis Brumancia, E. Sathyabama Institute of Science and Technology Chennai India M.I.E.T Engineering College Trichy India St. Joseph’s College of Engineering Chennai India Velammal Institute of Technology Chennai India

An innovative approach is elegantly launched to effectively identify the medical behavioural changes of the patients. With this end in view, the sequential change patterns are extracted at two diverse time intervals, with the help of the fuzzy time interval sequential change pattern mining employing the HGA technique. However, the pattern mining at two diverse time intervals is likely to yield further superfluous data. With an eye on averting the generation of the corresponding superfluous data, an optimized method such as the hybrid genetic algorithm (HGA) based fuzzy time interval sequential pattern mining is envisaged for the purpose of attaining the patterns. the sequential pattern detection algorithm effectively segments the located change patterns into four diverse types such as the perished patterns, added patterns, unexpected changes, and the emerging patterns. When the pattern categorization comes to an end, the changed patterns are harmonized by means of the similarity Computation index (sCi) values. At last, the significant patterns are estimated and employed to categorize the change in the conduct of the patient. the imaginative system is performed in the working stage of the MAtLAB and its execution is surveyed and appeared differently in relation to that of the advanced strategy like the genetic algorithm. © the Author(s).

关键词： Genetic algorithms

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