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Advanced UAV - based leaf disease detection: Deep Radial Basis Function Networks with multidimensional mixed attention

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Multimedia Tools and Applications 2024年 1-29页

作者： Das, Bappaditya Raghuvanshi, C.S. Research Scholar Department of Computer Science and Engineering FET Rama University Kanpur India Professor Department of Computer Science and Engineering FET Rama University Kanpur India

Plant disease causes a highly significant impact on production due to its destructive characteristics. A variety of chemical techniques should be applied to agriculture at different stages of disease infestation to enable precise plant protection. The integration of unmanned aerial vehicles (UAVs) for obtaining high-resolution images has become necessary due to the growing demand for precision agriculture, making crop health monitoring more efficient. In this study, we developed an innovative approach that combines the Deep Radial Basis Function Network (DRBFN) with the Multidimensional Mixed Attention (MMA) method. Since DRBFNs use radial basis functions as effective feature extractors, the model can detect even the smallest changes in the composition, color, and texture of leaves. This capability is particularly helpful when dealing with a diversity of plant species and pathogens exhibiting different symptoms. By deliberately considering pertinent spatial and spectral attributes, MMA is applied to enhance the discriminatory abilities of the network. By integrating spatial and spectral attention, the network can efficiently acquire information from both local and global sources, thereby enhancing the overall resilience and precision of the detection system. Our proposed model leverages UAV-captured images to achieve a classification accuracy of 96.95% in identifying leaf diseases across two different plant species. The integration of mixed attention mechanisms improved feature representation, leading to a 47.63% reduction in Root Mean Square Error (RMSE) compared to traditional methods. Furthermore, our model demonstrates significant efficiency by processing 2000 images, making it suitable for real-time applications. These results underscore the potential of UAV-based systems combined with advanced deep learning techniques for precision agriculture, offering scalable solutions for early disease detection and management. © The Author(s), under exclusive licence to S

关键词： Deep learning

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Multimodality in online education: a comparative study

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Multimedia Tools and Applications 2025年 1-34页

作者： Immadisetty, Praneeta Rajesh, Pooja Gupta, Akshita M. R, Dr. Anala A, Dr. Soumya Subramanya, Dr. K. N. Computer Science and Engineering R V College of Engineering Karnataka Bengaluru India Information Science and Engineering R V College of Engineering Karnataka Bengaluru India Principal and Professor R V College of Engineering Karnataka Bengaluru India

The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which students understand the subject and their response to the educational stimuli. The current systems consider only a single cue with a lack of focus in the educational domain. Thus, there is a necessity for the measurement of an all-encompassing holistic overview of the students’ reaction to the subject matter. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom while considering four cues, posture and gesture, facial, eye tracking and verbal recognition. In this paper, various machine learning models suitable for each cue are analyzed based on key metrics such as accuracy, ease of data collection, sensitivity, and limitations. The comparison of models revealed the most effective techniques for each cue. A multimodal approach derived from weighted majority voting is proposed by combining the top-performing models for each aspect of student engagement in online learning environments. This paper proposes a novel approach that integrates multiple modalities for affect recognition, aiming to improve the accuracy of student response assessments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Video conferencing

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Hybrid ASR for Resource-Constrained Robots: HMM-Deep Learning Fusion

Hybrid ASR for Resource-Constrained Robots: HMM-Deep Learnin...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Ranjan, Anshul Jegadeesan, Kaushik Iyer, Anantharaman Narayana Pes University Computer Science and Engineering Bangalore India Adjunct Professor at Pes University Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331533311

This paper introduces a hybrid Automatic Speech Recognition (ASR) system tailored for resource-constrained robots. The approach combines Hidden Markov Models (HMMs) with deep learning models, distributing processing tasks via socket programming. HMM-based processing resides within the robot, while a separate PC runs the deep learning model. The synergy of HMMs and deep learning optimizes speech recognition accuracy. Experiments on diverse robotic platforms demonstrate real-time, precise speech recognition. The system's adaptability to changing acoustic conditions and compatibility with lowpower hardware highlight its efficacy in environments with computational limitations. This hybrid ASR paradigm presents a promising path for seamless human-robot *** conclusion, this research contributes a pioneering dimension to robotics-oriented ASR techniques. By utilizing socket programming to distribute processing tasks across distinct devices, and strategically employing HMMs and deep learning models, the hybrid ASR system demonstrates its potential in enabling robots to adeptly comprehend and respond to spoken language, even in environments with restricted computational resources. This paradigm presents an innovative trajectory for seamless humanrobot interaction in various real-world contexts. © 2024 IEEE.

关键词： Hidden Markov models

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Lightweight group authentication protocol for secure RFID system

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Multimedia Tools and Applications 2024年第41期83卷 89249-89277页

作者： Kumar, Sanjeev Banka, Haider Kaushik, Baijnath Jharkhand Dhanbad India Jharkhand Dhanbad India Associate Professor Computer Science and Engineering SMVDU Jammu and Kashmir Katra India

Nowadays, wireless technology has been widely used in healthcare and communication systems. It makes our life easier in all respects. A radiofrequency identification device (RFID) has been deployed as a wireless and identity communication device. RFID is a low-resource device that requires cryptography with limited energy regarding the minimum key size. Security and authentication between the server and the tags are key challenges for an RFID system to maintain data privacy. This article presents the security vulnerabilities of recent existing RFID authentication schemes. Keeping the focus on stringent security, privacy, and low cost, we have designed a new lightweight group authentication protocol for the robust RFID system. A single server controls multiple tags by using the proposed lightweight protocol in the RFID system. Formal and informal security analysis is performed compared to other lightweight group authentication articles in which only informal security analysis is carried out. The formal security strength of our proposed protocol is analyzed using the AVISPA (Automated Verification of Internet Security Protocol Analysis) tool, confirming that it is safe from different security threats. The newly designed protocol’s performance analysis results are measured in terms of computational cost, storage space, and communication cost. Finally, the combined consequence of security and lightweight of the proposed group authentication protocol is superior and outperforms compared to the existing scheme. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Authentication

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A hybrid blended stacking disease prediction system based on symptoms

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Multimedia Tools and Applications 2025年第7期84卷 3439-3457页

作者： Indupalli, Manjula Rani Pradeepini, G. Research Scholar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India Professor Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh522 302 India

Traditional disease diagnosis methods often struggle with symptoms-based datasets containing categorical data, leading researchers to favor boosting algorithms like CatBoost for their computational efficiency;despite potential limitations in accuracy. To overcome these challenges, this study proposes a novel approach integrating ensemble boosting with traditional naive Bayesian techniques. Recognizing the risk of overfitting with pure ensemble methods, the model strategically blends non-linear models, including XGBoost, to assign weights to symptoms based on disease impact. The blending process, guided by XGBoost decision-making, significantly improves model accuracy compared to single-boost strategies. Preprocessing involves handling categorical data and missing values, enhancing the model's robustness. During the process of blending, the decision of XGBOOST plays an important role. The proposed model's accuracy of "97.2%" is significantly higher than the single-boost strategy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Accuracy Blended stacking CATegorical Boost Ensemble Multi classification Naive Bayesian

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Improving security performance of Internet of Medical Things using hybrid metaheuristic model

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Multimedia Tools and Applications 2025年第9期84卷 6403-6428页

作者： Kanneboina, Ashok Sundaram, Gopikrishnan Assistant Professor School of Engineering Malla Reddy University Telangana Hyderabad India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

The security of the Internet of Medical Things (IoMT) is very important because it connects many medical devices to improve care operations and patient monitoring in real time. Researchers have developed high-security frameworks with attribute-based access control, hashing, encryption, and privacy preservation. Current security models are either too complex or too unreconfigurable for real-time monitoring applications. Current models’ security performance depends on their internal configuration, which is not adaptable to new attacks. This research proposes an improved hybrid metaheuristic model to improve healthcare IoT security to overcome these restrictions. The model initially uses blockchain but can change its internal hashing and encryption standards. The proposed model continuously optimizes the blockchain-based IoMT deployment’s security and quality of service by combining Elephant Herding Optimisation (EHO) and Grey Wolf Optimisation. The proposed model uses dual fitness functions to improve security and service quality against various attacks. Multiple state-of-the-art models and DDoS, man-in-the-middle, masquerading, and Sybil attacks performed to evaluate the model. According to the comparison, the proposed model improved network consistency by 8.7%, throughput by 6.4%, packet delivery ratio by 8.2%, and attack detection and mitigation by 9.4%. Real-time healthcare use cases could use the suggested model after this performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Design and Implementation of IoT based flood alert monitoring system using microcontroller 8051

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Materials Today: Proceedings 2023年 80卷 2840-2844页

作者： Chaduvula, Kavitha Kranthi kumar, K. Markapudi, Babu Rao Rathna Jyothi, Ch. Assistant Professor Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Information Technology Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Professor and Head Department of Computer Science and Engineering Gudlavalleru Engineering College Andhraparadesh Gudlavalleru India Associate Professor Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and Technology Andhraparadesh Vijayawada India

In the Internet of Things (IoT) the physical objects network is described as "things" linked to sensors, software and other technologies to connect and share information through the Internet with other devices and systems. The floods lead to large losses of life and property in several countries. However, the absence of sufficient technology results in further death and property losses as a result of flooding in developed countries. This is because of a lack of flood alert systems. The objective of this paper is to monitor the flood situation and provide a text message warning in the event of a threat. The main aim of this paper is to detect rising river water levels at a safe distance away from the railways and to allow the respective authorities to take appropriate measures by means of SMS. A sensor is a system that senses and reacts to certain physical data. The 8051 microcontrollers were produced by Intel in 1981. The microcontroller is 8-bit. It has 40 DIP pins, 4 kb of ROM stores and 128 bytes of RAM and two timers and 16 bits. It is equipped with 2 timers. There are four parallel 8-bit ports that are programmable and adjustable as necessary. The hardware device that uses GSM mobile technology for the provision of an information link with a remote network is a GSM modem or a GSM module. A float switch is a level sensor type, a fluid control system that is used inside a tank. A condenser is a tool that stores electrical energy in an electric field. It's a passive electronic feature with two terminals. A LED is a semiconductor light source that transmits light through the flowing current. Liquid Crystal Display stands for LCD. It is a thin, flat display device used in a variety of electronics. A digital electronic level shifter is a system used in the transformation of signs from one logic or voltage domain to another, also known as logic level shifter or voltage level Translation. A diode is an electronic component with a two-terminal that mainly conducts curre

关键词： Microcontrollers

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm Driven by Machine Learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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EDRLNet: privacy preservation in online social networks via deep reinforcement learning and graph convolutional networks

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International Journal of Information Technology (Singapore) 2025年 1-10页

作者： Gowda, Nithish Ranjan Venkatesh Venugopal, K.R. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Associate Professor Department Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bangalore India University Visvesvaraya College of Engineering Bangalore University Bangalore India

Online social networks (OSN) have become extremely popular in the past few decades. Prominent OSN companies, such as Facebook and Twitter, control a huge amount of information on its users as well as interactions that may cause people to have major privacy concerns. The OSN information is combined with other online sources to reveal sensitive information. As a result, there is a great need to anonymize the OSN data before providing it to a third party. Thus, in this study, we propose a Deep Reinforcement Learning (DRL)-based rewiring operation that entails changing the relationships (edges) between nodes (users) without changing their degree to protect users’ privacy. To accomplish this, we introduce a novel technique for Enhanced DRL Networks called “EDRLNet” which combines an Optimized Policy Network (OPN) and Graph Convolutional Network (GCN) that preserves the essential characteristics of OSN data while anonymizing it to prevent privacy breaches. This approach employs a GCN to extract embeddings (features) from the edges and nodes. Furthermore, OPN applies the graph feature representation learned by the GCN model to policy learning. Additionally, the hyperparameters of OPN are optimized by using the Addax optimization algorithm (AOA). The suggested scheme’s performance is assessed using PageRank with mean-squared error (MSE), Root mean-squared error (RMSE), mean absolute error (MAE), Privacy score, Utility Score, Clustering coefficient, privacy and information loss. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Addax optimization algorithm Anonymization Deep reinforcement learning Graph convolutional network Online social network Privacy preservation

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