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Disruptive Technologies (ICDT), International Conference on

作者： Gorli Ramya Valli Kumari Vatsavayi Department Of Computer Science And Systems Engineering Andhra University College of engineering Visakhapatnam India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Rice serves as a primary food source for more than half of the global population. However, a range of diseases brought on by pathogens, including as bacteria, viruses and fungus are posing a growing threat to rice agriculture. These diseases have the potential to seriously reduce crop productivity and threaten food security. Conventional disease detection techniques mostly depend on the labor-intensive, time consuming and frequently prone to human error manual inspection performed by qualified agronomists. We concentrate on two cutting edge architectures: Vision Transformers (ViT) and DenseNet169, which is well-known for its high accuracy in image classification applications and efficiency in feature reuse which make use of self-attention mechanisms to identify complex patterns in images. We developed these models to precisely categorize various rice leaf diseases, including Bacterial Leaf Blight, Brown Spot, Leaf Blast, Leaf Scald, Narrow Brown Spot, Neck Blast, Rice Hispa, Sheath Blight, Tungro, and healthy leaves. This was achieved using a comprehensive dataset containing 15,910 annotated images of rice leaves. Our proposed systems improve crop management practices by enabling prompt interventions based on precise predictions but it helps to guarantee ensuring global food security and maximizing overall crop yield. This study highlights the revolutionary potential of artificial intelligence in agriculture, paving the way for further advancements that can enhance sustainability and productivity.

关键词： Productivity computer vision Pathogens Accuracy Food security Transformers Feature extraction Agriculture Sustainable development Diseases

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MediGuide AI using Machine Learning

MediGuide AI using Machine Learning

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： R Pooja Nikitha Elugubanti Mary Shiba C Kalai priya V K Sasirekha S Rathana Sabapathy Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Tiruvallur District Department Of Computer Science and Business Systems R.M.K Engineering College Kavaraipettai Department Of Computer Science and Engineering R.M.K College of Engineering and Technology Puduvoyal Tiruvallur District Department Of Computer Science and Business Systems R.M.D Engineering College Kavaraipettai Department Of Computer Science and Engineering Dhaanish Ahmed College of Engineering Vanchuvanchery Chennai District

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Providing diverse patient groups with their specific medication requirements, using a wide range of drugs, poses meaningful challenges for prescribing. Standardized approaches often lead to large waste, many side effects and suboptimal outcomes. Therefore, we need improved, more personalized healthcare. MediGuide AI's drug recommendation system is powered by machine learning and it offers a thorough solution. Optimized data pre-processing and analysis, leading to the desired outcome, is achieved using advanced machine learning algorithms like Naive Bayes, TF-IDF and the Natural Language Toolkit. Highly personalized prescriptions are generated using thorough patient data, including demographics and meaningful clinical factors to achieve substantially better accuracy. Important enhancements include dual recommendation outputs, which provide many drug recommendations alongside a large number of alternative Ayurvedic treatments presented side by side for effortless comparison. An advanced personalized dosage calculation module precisely determines drug dosages using advanced regression models based on age, weight and metabolism. This system helps patients track and record their medications and it also suggests several nearby Ayurvedic shops based on their location. MediGuide AI employs exceptionally powerful algorithms and its advanced tools deliver remarkably precise and exceptionally user-friendly healthcare solutions. Current systems make use of rule-based methods that cannot learn from new data, whereas MediGuide AI leverages a hybrid Naïve Bayes and PassiveAggressiveClassifier model for higher accuracy and real-time adaptability. It also includes Ayurvedic options to ensure personalized healthcare experience.

关键词： Drugs Accuracy Pain Databases Natural languages Machine learning Vectors Bayes methods Medical diagnostic imaging Diseases

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Air Pollutant Prediction Using Shallow Architecture Machine Learning Algorithms 8th

Air Pollutant Prediction Using Shallow Architecture Machine ...

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8th World Conference on Smart Trends in systems Security and Sustainability, WorldS4 2024

作者： Olaifa, Moses Malele, Vusumuzi Department of Computer Systems Engineering Faculty of ICT Tshwane University of Technology Pretoria South Africa Unit of Data Science and Computing Vaal Campus North-West University Vanderbijlpark South Africa

ISBN: (纸本)9789819786947

Over the past few decades, mortality rates associated with air quality pollution have risen in numerous countries around the globe. This pollution stems from different factors such as meteorological conditions, human activities from urban industrialization, vehicular emission, and so on. Air pollution constituents such as NO2, CO, SO, and coarse and fine particulate matter for different locations have been analyzed to learn both correlation and inter-dependency for long and short terms. However, many of these studies overlook datasets collected using low-cost tools and equipment. Most of the time, datasets of this nature are marred with inconsistencies and gaps at irregular intervals. This may render the air pollutant data unsuitable. In order to test the suitability of machine learning algorithms on such dataset, this study conducts a time-series forecast analysis using the prophet algorithm. Two additional models including linear regressor and random forest regressor are trained and tested on the same dataset and a comparative analysis of the three models is performed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Goal-Oriented Communication, Estimation, and Control Over Bidirectional Wireless Links

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IEEE Transactions on Communications 2025年第5期73卷 3031-3045页

作者： Cao, Jie Kurniawan, Ernest Boonkajay, Amnart Pappas, Nikolaos Sun, Sumei Popovski, Petar School of Electronic and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Institute of Infocomm Research Agency for Science Technology and Research Fusionopolis 138632 Singapore Linköping University Department of Computer and Information Science Linköping58183 Sweden Aalborg University Department of Electronic Systems Aalborg9220 Denmark

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost. © 1972-2012 IEEE.

关键词： Markov processes

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Maize leaf disease multiclass classification and recognition for sustainable agriculture using multi preceptive deep learning model

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Discover Sustainability 2025年第1期6卷 1-18页

作者： Gautam, Vinay Quraishi, Aadam AlGhamdi, Azzah Kaur, Gaganpreet Alghayadh, Faisal Byeon, Haewon Bhatt, Mohammed Wasim CUIET Chitkara University Punjab Rajpura 140401 India M.D. Research Intervention Treatment Institute Texas Houston United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh Saudi Arabia Convergence Department Korea University of Technology and Education Cheonan South Korea Model Institute of Engineering and Technology J&K Jammu India

Agriculture sector is faced with perennial challenges that threaten both its productivity and sustainability. Among the greatest threats to cereal crops is disease, especially on cereal grains such as maize. Maize is an important grain that has grown globally, yet it often falls prey to maize leaf disease, a destructive and prevalent disorder. The consequences of these diseases go beyond individual farmers;reduced yields destabilize supply chains, market stability, and global efforts towards creating sustainable food systems. The prevalence of leaf diseases adversely affects crop productivity, which directly impacts the objective of sustainable agriculture. In order to address this problem, technology, more specifically artificial intelligence, has been a game-changer. Adopting cutting-edge research for maize disease detection not only raises diagnostic accuracy but also supports sustainable agricultural practices. This approach encourages effective input use, supports food security, minimizes environmental degradation, and provides farmers with accurate tools, all of which contribute to long-term agricultural resilience and sustainability. However, the incorporation of AI in farming is confronted by a number of challenges. Numerous hindrances hinder precise detection and categorization of maize leaf diseases via artificial intelligence methods. In tackling the limitations, the current study introduces an AI-based approach. It applied Multi-scaled Xception pre-trained models to extract deep features from images. The models were fine-tuned with varying weights for advancing the feature extraction so as to enhance the likelihood of correct visual classification. In addition to its strong accuracy, the research provides a formal and strict mathematical formulation, and different optimization methods further establish the effectiveness of the model. Additionally, the examination of fusion operators helps to improve the interpretability of the model. The expected model w

关键词： CCA Deep learning Feature extraction Feature Fusion Xception

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd International Conference on Next-Generation Computing and Information systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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The Information systems and Model for Succession Planning in Higher Education Institutions

The Information Systems and Model for Succession Planning in...

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computer Applications (ICCA), IEEE Conference on

作者： Devyano Luhukay Ford Lumban Gaol Meyliana Harjanto Prabowo Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Succession planning is important in organization management. It is a strategic level management that involves not only finding the best successor but also including the development program for the successor so that they will be ready for the new prospective position. The integration and collaboration between all related stakeholders are important. Specifically, in succession planning at higher education, key positions play an important role so that institutional operations can run well. In this research, a succession planning model in higher education was built using the Systematic Literature Review(SLR), and the Software Development Lifecycle(SDLC) method was used for the application development, also, to be able to evaluate the implementation of one of the steps of the model, the simulation technique used machine learning (ML) to find the best algorithm for predicting the readiness of potential successors at higher education. Six algorithms were used: Logistic Regression, Decision Tree, K-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. The data prepared for the simulation is based on the factors for the best successor candidate. The results obtained in this research are the succession planning model and the application design, as well as the Support Vector Machine (SVM) algorithm as the best algorithm for predicting the readiness status of successor candidates based on SVM’s accuracy score that is compared to the other algorithm in some variance amounts of data. The Succession Planning Model that is actualized in the application design provides a reference for the higher education institution to implement the Succession Model in their institution.

关键词： Support vector machines Machine learning algorithms Systematics Education Software algorithms Machine learning Predictive models Prediction algorithms Planning Information systems

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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Correction to: Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes (Neural Computing and Applications, (2025), 10.1007/s00521-025-10991-3)

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Neural Computing and Applications 2025年 1-2页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

In this article, the author’s name Stephen Ojo was incorrectly written as Stepehn Ojo and the affiliation details for author Stephen Ojo was incorrectly given as ‘department of Electrical and computer engineering, College of engineering Anderson, Anderson University, Anderson, SC 29621, USA’ but should have been ‘department of Electrical and computer engineering, College of engineering, Anderson University, Anderson, SC 29621, USA’. In addition, the affiliation details for author Gabriel Avelino Sampedro were incorrectly given as ‘College of computer Studies, De La Salle University, 2401 Taft Ave., Malate, 1004 Manila, Philippines and Faculty of Information and Communication Studies, University of the Philippines Open University, 4031 Los Baños, Philippines’ but should have been ‘department of computer science, University of the Philippines Diliman, 1101 Quezon City, Philippines’. The original article has been corrected. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

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Generalized Simultaneous Perturbation-Based Gradient Search With Reduced Estimator Bias

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IEEE Transactions on Automatic Control 2025年

作者： Pachalyl, Soumen Bhatnagarz, Shalabh Prashanth, L.A. TCS Research Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Indian Institute of Science Department of Computer Science and Automation The Robert Bosch Centre for Cyber Physical Systems Bangalore India Indian Institute of Technology Madras Department of Computer Science and Engineering Chennai India

We present a family of generalized simultaneous perturbation-based gradient search (GSPGS) estimators that use noisy function measurements. The number of function measurements required by each estimator is guided by the desired level of accuracy. We first present in detail unbalanced generalized simultaneous perturbation stochastic approximation (GSPSA) estimators and later present the balanced versions (B-GSPSA) of these. We extend this idea further and present the generalized smoothed functional (GSF) and generalized random directions stochastic approximation (GRDSA) estimators, respectively, as well as their balanced variants. We show that estimators within any specified class requiring more number of function measurements result in lower estimator bias. We present a detailed analysis of both the asymptotic and non-asymptotic convergence of the resulting stochastic approximation schemes. We further present a series of experimental results with the various GSPGS estimators on the Rastrigin and quadratic function objectives. Our experiments are seen to validate our theoretical findings. © 1963-2012 IEEE.

关键词： Stochastic systems

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