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IoT based crop rotation and soil nutrition analysis

Materials Today: Proceedings 2022年 64卷 590-597页

作者： Raj, V.Arun koppula, Neeraja Lavanya, M. Manjari, R.Kavin Department of Electronics and Communication Engineering Mepco Schlenk Engineering College Sivakasi India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad India

Crop revolution is the act of growing a progression of various sorts of harvests in a similar region rather than the one yield framework. This strategy for development normally recharges the dirt supplements and lessens synthetic contamination because of overabundance utilization of manures. Large scale supplements like Nitrogen (N), Phosphorous (P) and Potassium (K), soil pH, temperature and mugginess assume a significant part in deciding the harvest to be developed straightaway. In our proposed framework, the degree of full scale supplements NPK and pH is observed in light of the force of variety change in the dirt example arrangement utilizing a variety sensor. Along with this, the temperature, humidity and soil moisture are also monitored to maintain the soil health. The data sensed by these sensors is stored on the cloud and analysed based on which suggestions for growth of the suitable crop are made. A web portal is designed which displays a list of particular crops suitable for the tested soil. Additionally, the current soil moisture level, atmospheric temperature and humidity are also displayed. This system is designed so that any individual can implement crop rotation technique in their back yards, roof top garden or in their own farms with reduced usage of fertilizers. © 2022

关键词： Crop rotation

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Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

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Intelligent Automation & Soft Computing 2023年第5期36卷 2055-2070页

作者： A.Ramachandran P.Ramadevi Ahmed Alkhayyat Yousif Kerrar Yousif Department of Computer Science and Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirapalli620025India College of Technical Engineering The Islamic UniversityNajafIraq Department of Computer Technical Engineering Al-Hadba University CollegeMosulIraq

Nowadays,numerous applications are associated with cloud and user data gets collected globally and stored in cloud *** addition to shared data storage,cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud,public cloud,community cloud and private *** cloud-based computing solutions are highly con-venient to the users,it also brings a challenge i.e.,security of the data ***,in current research paper,blockchain with data integrity authentication technique is developed for an efﬁcient and secure operation with user authentica-tion *** technology is utilized in this study to enable efﬁcient and secure operation which not only empowers cloud security but also avoids threats and ***,the data integrity authentication technique is also uti-lized to limit the unwanted access of data in cloud storage *** major objec-tive of the projected technique is to empower data security and user authentication in cloud computing *** improve the proposed authentication pro-cess,cuckooﬁlter and Merkle Hash Tree(MHT)are *** proposed meth-odology was validated using few performance metrics such as processing time,uploading time,downloading time,authentication time,consensus time,waiting time,initialization time,in addition to storage *** proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

关键词： Blockchain security data integrity authentication cloud computing signature hash tree

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Optimal Feature Selection for Imbalanced Text Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 135-147页

作者： Khurana, Anshu Verma, Om Prakash Delhi Technological University Department of Computer Science and Engineering Delhi110042 India Delhi Technological University Department of Electronics and Communication Delhi110042 India

Textual data suffers from two main problems, large number of features and class imbalance. Many conventional approaches and their variants exist in literature to solve both these problems. The classic synthetic minority oversampling technique (SMOTE) is the most explored technique for balancing the dataset. We introduced a new algorithm to balance the dataset, named distributed SMOTE (D_SMOTE), which overcomes the problem of lack of density and reducing the formation of small disjuncts. Further, another problem handled is the large number of features or high-dimensionality. To solve high-dimensionality, a novel feature selection technique is introduced known as modified biogeography-based optimization (M_BBO). The proposed model, M_BBO, performs modification in ranking of variables using feature weighting algorithm rather than randomly ranking. We have proposed two new expressions in D_SMOTE and one new expression in M_BBO. The extensive experimental results are computed out on four text classification datasets with four machine learning classifiers. The results are concluded using three performance measures, area under curve, G-mean, and F1-score. Our empirical and statistical observation for four class-imbalanced datasets shows that the proposed D_SMOTE outperforms the other similar oversampling technique. We have also compared our proposed algorithm, M_BBO+D_SMOTE, with other models on 17 imbalanced text classification datasets. Our model outperformed the other models in 14 datasets. We have also compared our model with bidirectional encoder representations from transformers. To validate the experimental analysis, statistical Friedman test is employed. © 2020 IEEE.

关键词： Support vector machines

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Exploring Learning-based Approaches for Enhancing IoT Networking Through Big Data Processing 15

Exploring Learning-based Approaches for Enhancing IoT Networ...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Pandey, Arvind Kumar Devrani, Rekha Sunil, M.P. Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

In current years, the internet modern day (Iota) has a visible fantastic boom. With the advancement of modern Iota technologies, new possibilities emerge for better and greater green networking and the usage of records analytics. This paper explores modern-day-based techniques so that it will leverage huge statistics processing for improving Iota networks. We have become aware of diverse approaches for community optimization, such as gadget cutting-edge algorithms, deep learning models, and different heuristics-primarily based strategies. We also define the advantages and downsides of contemporary techniques and discuss their capacity programs for Iota-like networks. We further analyze the capacity of those strategies in huge data processing and compare their overall performance and scalability. The paper concludes with a discussion of implications to the networking network and the way these methods can be implemented for higher performance in today's Iota networks. © 2024 IEEE.

关键词： Analytics Data Internet Iot Networks Performance Statistics Techniques

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Multimodal Transformer Fusion for Sentiment Analysis using Audio, Text, and Visual Cues 1

Multimodal Transformer Fusion for Sentiment Analysis using A...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Peerbasha, S. Habelalmateen, Mohammed I. Ramachandra, A.C. Revathi, P. Saravanan, T. Affiliated to Bharathidasan University Department of Computer Science Tiruchirapalli India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In modern era, the increased growth in social media platforms and technologies such as Artificial Intelligence (AI) have gained interest towards multimodal sentiment analysis that includes text, audio and visual cues for extraction of useful insights. Although these systems are capable of analyzing sentiment, but also faces certain challenges in synchronization of multimodal inputs, data fusion and reliability under various contexts. Hence, this research proposes an effective Multimodal Transformer Fusion (MTF) which combines the strengths of various modalities to recognize human emotions. Initially, the data is collected from CMU-Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset. Further, the data is preprocessed with Empirical Mode Decomposition (EMD) and Min-max normalization, stop words removal, and lemmatization to remove noise and improve the quality of sentiment analysis. Then, the features are extracted for different input modalities by using Mel-Frequency Cepstral Coefficients (MFCC), 3D Residual Network (ResNet 3D) and Distilled Bidirectional Encoder Representations from Transformers (DistilBERT). After that, extracted features are further fed into proposed MTF to classify the sentiment of a customer into positive, negative or neutral. From the results, the proposed MTF achieved outstanding results in F1 Score (80.76%) as well as Mean Absolute Error (0.376) when compared with the existing Tensor Fusion Network (TFN). © 2025 IEEE.

关键词： Empirical mode decomposition

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Enhancing Blurred Image Processing with IoT Integration for Improved Clarity 2

Enhancing Blurred Image Processing with IoT Integration for ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Tummalapalli, Geetamma Summia Parveen, H. Ramesh, S.M. Monica, P. Santhiya, B. Rajasekaran, Arun Sekar GMR Institute of Technology Department of Electronics and Communication Engineering Rajam India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India GITAM Deemed to be University Department of Electronics and Communication Engineering GST Visakhapatnam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India SR University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798331534950

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains. © 2024 IEEE.

关键词： Edge computing

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Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

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computers, Materials & Continua 2023年第1期74卷 1523-1540页

作者： Sandeep Kumar MohdAnul Haq Arpit Jain C.Andy Jason Nageswara Rao Moparthi Nitin Mittal Zamil S.Alzamil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAP522502India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Computer Science and Engineering Teerthanker Mahaveer UniversityMoradabadUttar Pradesh244001India Department of Electronics and Communication Engineering Sreyas Institute of Engineering and TechnologyHyderabad500068India University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia

Day by day,biometric-based systems play a vital role in our daily *** paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert *** proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,*** proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and *** key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and *** proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and *** comparing with existing technologies,the proposed algorithm gave a better error rate and less *** rate and time is decreased by 19.79%,5.13 *** the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT *** proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

关键词： Speech emotion recognition classifier implementation feature extraction and selection smart assistance

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A Patient Response Prediction System using Multi-Scale Feature Fusion Semantic Segmentation Network and Med-YOLO 1

A Patient Response Prediction System using Multi-Scale Featu...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Shankari, S. Uma Ali, Hassan Mohamed Durga, Boya Vijaya Vadivu, M. Senthil Mangaiyarkarasi, V. Srm Institute of Science and Technology Ramapuram Department of Computer Science and Applications Chennai India College of Technical Engineering the Islamic University Department of Computers Techniques Engineering Najaf Iraq Maisammaguda School of Computer Science and Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

Nowadays, most of the people are getting affected with various health issues such as typhoid, diabetes and cancer. A human immune system plays a crucial role in maintaining and balancing human health and reduces the risk of getting ill. Immunotherapy is a treatment which utilizes human body immunity power to fight cancer. There are many challenges in the existing models such as imbalance data, heterogenic data, uncertainties, and lack of temporal data. These challenges are resolved by imposing deep learning techniques and generating a valid information with the clinical details of patient health conditions. Therefore, a hybrid model name called Multi-Scale Feature Fusion Semantic Segmentation Network (MSSNet) and You Look Only Once (MedYOLO) for feature extraction and detection. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is employed to overcome the issue of imbalanced data;further MSSNet is imposed for extracting the optimal features and detected using MedYOLO. Also, to get detailed information and generate cohort, the data collected from GSC hospital with overall details that are essential to predict the patient response to immunotherapy in cancer treatment. In the end, MSSNet-MedYOLO obtained higher accuracy of 98.16% for an effective prediction of patient response which is better than existing Convolutional Neural Network (CNN). © 2024 IEEE.

关键词： Semantic Segmentation

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Reliable Smart Wrist Pulse Oximeter for Hypoxemia and COVID-19 Patients 8th

Reliable Smart Wrist Pulse Oximeter for Hypoxemia and COVID-...

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8th International Conference on Inventive communication and Computational Technologies, ICICCT 2024

作者： Ramamoorthy, Raghu Smitha, J.A. Velu, Anitha Department of Computer Science and Engineering The Oxford College of Engineering Karnataka Bengaluru India Department of Computer and Engineering Sri Sairam College of Engineering Karnataka Bengaluru India Department of Electronics and Communication Engineering Sri Sairam College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819777099

In the present day, the people are living with the COVID-19. The catastrophic virus of COVID-19 causes hypoxemia (shortness of breath), sometimes even severe cases. Hypoxemia is caused due to low oxygen saturation (SpO2) levels and to monitor SpO2 levels this work proposes a reliable smart wrist pulse oximeter. Patients who are diagnosed during the early stages of the virus are able to monitor their SpO2 levels at home and take the prescribed medicines. Oximetry follows the principle of light reflection. The fingertip model is the most common technique used to display the blood oxygenation level. However, the finger model is not as comfortable and does not promote continuous monitoring of SpO2 levels. Factors such as nail polish can play a role in the accuracy of the reading. The proposed design is a non-invasive medical device that can measure the oxygen saturation level in a person’s blood. The wrist wearable devices are more convenient for continuous monitoring of oxygen saturation levels than finger models. Most devices in application today are inaccurate and used for non-clinical use. Therefore, it is aimed to achieve better accuracy by avoiding readings that are influenced by factors such as motion artifacts by utilizing sensors that are appropriate through an accelerometer and gyroscope. The proposed oximeter can initiate notifications to alert an individual of the levels of oxygen saturation, fall detection, and heart rate levels. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Oximeters

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Emotion-driven Activity Detection: Detecting Activities Perception through Facial Feature Analysis in Educational Environments 18

Emotion-driven Activity Detection: Detecting Activities Perc...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Balasubramanian, K. Mariadass, Emmanuel Peo Akalya, L. Cinthuja, K. Murali, N. Rajangam, Balamurugan E. G. S. Pillay Engineering College Department of Computer Science and Engineering Nagapattinam India Panimalar Engineering College Department of Computer Science and Engineering Chennai India K. Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Trichy India

ISBN: (纸本)9789380544519

Understanding and categorizing student activities within educational settings is a critical component of enhancing learning outcomes and well-being. This study introduces an innovative approach to student activity classification by leveraging facial emotion recognition technology. The system is designed to capture and analyze students' facial expressions, interpret their emotional states, and subsequently categorize their activities. By deploying a dataset encompassing a wide range of academic and non - academic activities, this research explores the purpose of deep learning models for recognition of facial emotion. The system is capable of identifying emotions such as sadness, surprise, happiness and anger. Extracted emotion features are then utilized to classify student activities, shedding light on whether a student is engaged, attentive, confused, or disinterested, among other states. This approach holds the potential to enhance educational environments by providing real-time insights into student behaviour and facilitating timely interventions to improve learning experiences and outcomes. Additionally, it opens up avenues for personalized educational support and the development of intelligent learning systems. In this paper we could implement the system to extract the facial features using Grassmann algorithm. And identify the emotions of student at predefined time intervals. Based on emotion classification, predict the active status and send reports to admin. Furthermore, this technology holds promise for the development of intelligent learning systems that adapt to students' emotional states, providing additional support or challenges when needed. For instance, a virtual tutor could adjust the difficulty of exercises based on a student's emotional responses, creating a dynamic and responsive learning experience. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep learning Face recognition system Features based matching Slot allocation Smart parking system

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