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An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

Recent Advances in computer science and Communications 2024年第2期17卷 1-12页

作者： Pandey, Kavita Chandak, Abhimanyu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the im-portant findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance. © 2024, Bentham science Publishers. All rights reserved.

关键词： Losses

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Farm-Level Smart Crop Recommendation Framework Using Machine Learning

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Annals of Data science 2025年第1期12卷 117-140页

作者： Bhola, Amit Kumar, Prabhat Department of Computer Science and Engineering National Institute of Technology Patna Bihar Patna India

Agriculture is the primary source of food, fuel, and raw materials and is vital to any country’s economy. Farmers, the backbone of agriculture, primarily rely on instinct to determine what crops to plant in any given season. They are comfortable following customary farming practices and standards and are oblivious to the fact that crop yield is highly dependent on current environmental and soil conditions. Crop recommendations involve multifaceted factors such as weather, soil quality, crop production, market demand, and prices, making it crucial for farmers to make well-informed decisions. An improper or imprudent crop recommendation can affect them, their families, and the entire agricultural sector. Modern technologies like artificial intelligence, machine learning, and data science have emerged as efficient solutions to combat issues like declining crop production and lower profits. This research proposes a Smart Crop Recommendation framework that leverages machine learning to empower farmers to make informed decisions about optimal crop selection. The framework consists of two phases: crop filtration and yield prediction. Crops are filtered in the first phase using an artificial neural network based on local input parameters. The second phase estimates yield for filtered crops, considering the season, farm area, and location data. The final recommendation provides farmers with crops aimed at maximizing profit. The remarkable 99.10% accuracy of the framework is demonstrated through experimentation using artificial neural networks and the 0.99 R2 error metric for the random forest. The uniqueness of this framework lies in its distinctive focus on the farm level and its consideration of the challenges and various agricultural features that change over time. The experimental results affirm the effectiveness of the framework, and its lightweight nature enhances its practicality, making it an efficient real-time recommendation solution. © The Author(s), under exclusi

关键词： Crops

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Generating Long Financial Report Using Conditional Variational Autoencoders With Knowledge Distillation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1669-1680页

作者： Wang, Ziao Ren, Yunpeng Zhang, Xiaofeng Wang, Yiyuan Harbin Institute of Technology Department of Computer Science Engineering Shenzhen518055 China

Generating financial reports from a piece of news is a challenging task due to the lack of sufficient background knowledge to effectively generate long financial reports. To address this issue, this article proposes a conditional variational autoencoders (CVAEs)-based approach that distills external knowledge from a set of news-report data. Specifically, we design an encoder-decoder architecture to learn the latent variable distribution from this set of news-report data to provide background knowledge. Next, a teacher-student network is employed to distill knowledge to refine the output of the decoder component. To evaluate the model performance, extensive experiments have been performed on two public datasets using evaluation criteria like BLEU, ROUGE, METEOR, and human evaluation. Our promising experimental results demonstrate that our proposed approach outperforms existing state-of-the-art approaches. © 2020 IEEE.

关键词： Natural language processing systems

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Analysis of IoT Applications in Highly Precise Agriculture Farming

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Recent Patents on engineering 2025年第2期19卷 71-81页

作者： Reddy N, Latha Masila P, Gopinath School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore632014 India

Background: The IoT (Internet of Things) assigns to the capacity of Device-to-Machine (D2M) connections, which is a vital component in the development of the digital economy. IoT integration with a human being enables real-time decision-making in communication, collaboration, and technology analytics. Furthermore, environmental factors impacting plants, such as air humidity, temperature, air quality index, and soil wetness, are not frequently documented, emphasizing the development of a data monitoring system for future agricultural research and development. Methods: An IoT-based Intelligent Farming System is proposed. An innovative IoT-based intelligent farming system is developed that integrates real-time data monitoring, machine learning algorithms, and IoT technology to address the identified gaps and challenges. Results: In the face of climate change, extreme weather, and environmental constraints, increased food demand must be satisfied. Intelligent agriculture enabled by IoT technology can reduce waste and increase productivity for producers and farmers, from fertilizer use to tractor trips. Conclusion: In conclusion, this patent paper provides insightful and informative commentary on the progress made in technology within the agriculture industry and the challenges that still need to be overcome to achieve optimal outcomes. © 2025 Bentham science Publishers.

关键词： IoT agriculture soil quality weather conditions machine learning environmental constraints

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Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

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Discover Internet of Things 2024年第1期4卷 16页

作者： Walling, Supongmen Lodh, Sibesh Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. © The Author(s) 2024.

关键词： Network intrusion

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IoT based Predictive Modeling Techniques for Cancer Detection in Healthcare Systems

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Recent Patents on engineering 2025年第2期19卷 82-94页

作者： Ramya, T. Gopinath, M.P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Background: The main objective of the Internet of Things (IoT) has significantly influenced and altered technology, such as interconnection, interoperability, and sensor devices. To ensure seamless healthcare facilities, it's essential to use the benefits of ubiquitous IoT services to assist patients by monitoring vital signs and automating functions. In healthcare, the current state-of-the-art equipment cannot detect many cancers early, and almost all humans have lost their lives due to this lethal sickness. Hence, early diagnosis of cancer is a significant difficulty for medical experts and researchers. Methods: The method for identifying cancer, together with machine learning and IoT, yield reliable results. In the Proposed model FCM system, the SVM methodology is reviewed to classify either benign or malignant disease. In addition, we applied a recursive feature selection to identify characteristics from the cancer dataset to boost the classifier system's capabilities. Results: This method is being applied in conjunction with fuzzy cluster-based augmentation, and classification can employ continuous monitoring to forecast lung cancer to improve patient care. In the process of effective image segmentation, the fuzzy-clustering methodology is implemented, which is used for the goal of obtaining transition region data. Conclusion: The Otsu thresholding method is applied to help recover the transition region from a lung cancer image. Furthermore, morphological thinning on the right edge and the segmentation-improving pictures are employed to increase segmentation performance. In future work, we intend to design a prototype to ensure real-time analysis to provide enhanced results. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Lung cancer

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Refinement modeling and verification of secure operating systems for communication in digital twins

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Digital Communications and Networks 2024年第2期10卷 304-314页

作者： Zhenjiang Qian Gaofei Sun Xiaoshuang Xing Gaurav Dhiman School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely *** verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented *** this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly *** verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

关键词： Theorem proving Isabelle/HOL Formal verification System modeling Correctness verification

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Supportive particle swarm optimization with time-conscious scheduling (SPSO-TCS) algorithm in cloud computing for optimized load balancing

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 192-198页

作者： Menaka, M. Sendhil Kumar, K.S. School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India

Task scheduling for virtual machines (VMs) has shown to be essential for the effective development of cloud computing at the lowest cost and fastest turnaround time. A number of research gaps about job schedule optimization are included in the current paper. A thorough analysis of the data generated by this activity is essential to resolving the resource allocation mechanism of the cloud architecture. To fully utilize virtual machines with a similar weight distribution, a strategy-oriented mixed support and load balancing structure has been developed in this work. To minimize make-span time and accomplish initial load balancing, the SPSO-TCS technique combines Time-Conscious Scheduling with Supportive Particle Swarm Optimization. Finding the optimal make span time minimization for each virtual environment is the aim of this stage. Its main objective is to discover the sequence of activities with the least computation time and to reduce the time required to finish each operation. Utilizing the hybrid idea leads to a decrease in makespan and the use of the least amount of energy. © 2024

关键词： Virtual machine

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Cyberbullying detection in Hinglish comments from social media using machine learning techniques

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Multimedia Tools and Applications 2024年第36期83卷 84025-84046页

作者： Kumar, Saurav Mondal, Mrinmoy Dutta, Tanuja Singh, Thoudam Doren Computer Science and Engineering National Institute of Technology Assam Silchar788010 India

With the development of the Internet, the use of social media has increased dramatically over time and has emerged as the most powerful networking tool of the twenty-first century. From youngsters of ten years to senior citizens of sixty years, everyone is profoundly active in social media. Social media, due to its easy accessibility, has become a major part of our lives in all segments. Social media became a crucial platform for communication during the COVID-19 pandemic as people were socially isolated and had little access to others. The use of social media platforms helped keep the world connected. Those who were stuck at home alone turned to social media to keep in touch with friends and find entertainment. However, with the positives of social media come along a very dark negative side too. The greater involvement of social media has given rise to cyberbully where someone bullies or harasses others over social media. People, especially teenagers are found to write and post negative comments in facebook, instagram, youtube etc. This has become a major social cause as such activities are extremely disturbing for the victim. In this paper, we have proposed a comparative study between various machine learning frameworks used to detect cyberbullying in social media comments. In addition, the comments are also classified according to the severity of cyberbullying. The main contribution of this work is to collect a sizable data or comments based on the Hinglish language (a mixture of English and Hindi terms written in Latin script) and then detect cyberbullying in the Hinglish language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Machine learning

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A Study of Federated Learning with Internet of Things for Data Privacy and Security using Privacy Preserving Techniques

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Recent Patents on engineering 2024年第1期18卷 1-17页

作者： Shakeer, Shaik Mahamad Rajasekhara Babu, M. School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India

To address the privacy concerns that arise from centralizing model training on a large number of IoT devices, a revolutionary new distributed learning framework called federated learning has been developed. This setup of devices works together to train models without compromising the security of individual data streams. To solve machine learning problems, a federated learning architecture relies on many clients working together through a central aggregator. To protect each device's privacy, we only access the training data at random. Because of its focus on decentralized processing and model transmission, federated learning considerably reduces systemic privacy risks and costs in comparison to conventional centralized machine learning systems. All client data is encrypted before being stored in a local database. Each user's device collects data locally for training during a federated learning session, and then uploads the final model to a centralized server. To provide a comprehensive assessment and inspire more research, this paper explores into the inner workings of federated learning from five different vantage points: data partitioning, privacy approaches, machine learning models, communication architectures, and system heterogeneity. Then, suggestions for further research are given, and a list is compiled of the most recent problems in federated learning. We conclude with an examination of the present applications of federated Learning knowledge. © 2024 Bentham science Publishers.

关键词： Privacy-preserving techniques

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