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Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

Remote Sensing in Earth Systems Sciences 2024年第4期7卷 389-398页

作者： Vaithianathan, V. Rebecca, B. Begum, M. Fathima Sundarraj, Antony Allwyn Kumar, M. Prema Kiran, Ajmeera Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai 603110 India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana Dundigal 500043 India Assistant Professor Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology and Science Andhra Pradesh-517325 Madanapalle India Department of Food Technology Sri Shakthi Institute of Engineering and Technology Coimbatore Tamilnadu Coimbatore India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The primary sector of our Indian economy is agriculture. Today’s world’s most important and vital industries are agriculture and irrigation. Information and communication technology must be used in our agriculture sectors in order to support farmers and agriculturalists in improving their practices across the entire crop-growing and post-harvest processes. It is still challenging to define processed food, even in light of mounting research that suggests consuming more ultra-processed food has negative health effects. This research proposes novel technique in smart agriculture model based on cloud machine learning in cloud machine learning and healthcare system. By leveraging IoT and cloud-based data to create data-driven product decisions, cloud computing in agriculture can assist farmers with smart agriculture practices. Here, the input has been collected as food quality based on smart agriculture using a cloud model as well as processed for noise removal and normalisation. Then, based on this data, the healthcare security system has been analysed using reinforcement adversarial stacked encoder with Markov fuzzy algorithm. The experimental analysis has been carried out for various food quality datasets in terms of training accuracy, random precision, normalised correlation ratio, F-1 score, and AUC. The proposed technique achieved training accuracy of 96%, F-1 score of 94%, random precision of 97%, normalised correlation ratio of 93%, and AUC of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial stacked encoder Cloud machine learning Cloud machine learning Healthcare system Smart agriculture

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Precaution Chatbot for Epilepsy Patients using Natural Language Processing and Deep Learning Sequential Model

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Procedia computer Science 2024年 233卷 537-546页

作者： E Kasthuri S. Subbulakshmi Rajasree Sreedharan Research Scholar Department of Computer Science and Engineering Assistant Professor Department of Computer Science and Applications Amrita School of Computing Amritapuri India Associate Professor Division of Nephrology Department of Pediatrics Medical College of Wisconsin Milwaukee Wisconsin USA

Epilepsy is a brain disorder that can affect people of all ages which is one of the most common neurological disorders affecting around 50 million individuals. Healthcare Chatbots schedule appointments, respond to patient inquiries, and offer relevant information, freeing medical staff to handle life crises. This paper implements a chatbot for epilepsy patients to increase awareness about epilepsy. Patients may easily access this AI-based healthcare chatbot whenever they have queries regarding precautions to be taken by epilepsy patients. Chatbot uses a rich set of epilepsy-related datasets collected from trusted websites which are preprocessed with NLP techniques to prepare the data for model ***, LSTM, and RNN models are created and evaluated to produce answers to user queries related to possible precautions to avoid seizures, safety measures to be taken by patients with seizure history, etc. Evaluation of models shows that the RNN model generates more accurate answers with 94% accuracy. Finally, the epilepsy question answering Chatbot for precaution is concluded with the RNN model.

关键词： Epilepsy Seizure Chatbot Natutal Language Processing LSTM Long short-term memory Recurrent neural network Deep neural network

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Insightful Clinical Assistance for Anemia Prediction with Data Analysis and Explainable AI

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Procedia computer Science 2024年 233卷 45-55页

Anemia among young people has positive correlation with susceptibility and impede cognitive development. As per WHO 2019 statistics global prevalence rate of anemia is 33 percent. This work aims to predict anemia with inferences to identify the root cause of anemia. Prediction is done with machine learning algorithms with proper pre-processing, data normalization, and class imbalance approaches. Explainable AI, a transparent model, is used to enhance the prediction which enables to draw inferences. LGM Boost learning model prediction has a 91% accuracy and explainable AI framework predictions of most contributing attribute is accurate when compared to other models. This approach helps healthcare professionals make informed decisions on the most optimal treatment for anemia.

关键词： Explainable Artificial Intelligence Machine learning Anemia Data preprocessing

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Deep Learning Techniques Advancements in Apple Leaf Disease Detection

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Procedia computer Science 2024年 235卷 713-722页

作者： E Kannan Carmel Mary Belinda M J Alex David S Ruth Naveena N Almas Begum Hemalatha D Professor Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Chennai Tamilnadu India 602105 Assistant Professor (SG) Hindustan Institute of Technology and Science Chennai Tamilnadu India 603103 Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062

An exhaustive and comprehensive investigation was undertaken to address the critical issue of disease detection on apple leaves using cutting-edge deep learning techniques. The research delved into an array of diverse approaches, meticulously examining their efficacy and performance in disease detection, ultimately offering valuable insights into this vital domain. The research effort was marked by the exploration and application of a wide spectrum of deep learning models, each chosen for its distinct characteristics and potential advantages. The results of this extensive work were nothing short of remarkable. This study uses state-of-the-art deep learning techniques to present a thorough and rigorous analysis into the important problem of disease detection on apple leaves. Our research covers a wide range of approaches, all of which have been thoroughly assessed for their efficacy in the diagnosis of disease. We used a wide range of deep learning models, selected for their special qualities and possible benefits. The results of this extensive study are impressive and measurable. VGG-INCEP, the top approach, showed exceptional performance with a measured accuracy rate of 97%. The quantification of precision, recall, and F1 scores were 0.94, 0.92, and 0.92, respectively. Similarly, InceptionV3 yielded an F1 score of 0.93, precision of 0.95, and recall of 0.91, in addition to a measured accuracy of 97%. AlexNet consistently demonstrated measurable high precision (0.95) and recall (0.93), resulting in an F1 score of 0.93, despite a somewhat lower accuracy of 87%. The method's balanced performance is highlighted by these metrics. The study also evaluated the effectiveness of SVM, MobileNet, RCNN, and a recommended method. With quantifiable accuracy of 98% and quantifiable precision, recall, and F1 scores of 0.96, the suggested technique stood out. This assessment unequivocally shows that the suggested approach produces the best accuracy and overall performance and is di

关键词： Apple leaf diseases Deep learning Disease detection Precision agriculture Performance evaluation

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Deep Reinforcement Learning for Energy Efficiency Optimization using Autonomous Waste Management in Smart Cities

Deep Reinforcement Learning for Energy Efficiency Optimizati...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： T. Kavitha Koushik Reddy Chaganti Sivananda Lahari Reddy Elicherla M. Ravi Kumar D. Chaithanya K Manikanta Assistant Professor Department of CSE Srinivasa Ramanujan Institute of Technology Research Scholar Dept. of Computer and Information Systems University of the Cumberlands Associate Professor Department of CSE Dayananda Sagar University Assistant Professor Department of Cyber Security & IoT Malla Reddy University Assistant Professor Department of CSE CVR College of Engineering Assistant Professor Department of CSE Gates Institute of Technology

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Smart cities face vital waste management challenges regarding efficiency and sustainability in their development period. The established waste collection approaches that depend on pre-set schedules together with unchanging routes create problems by possessing both inefficient resource usage and elevated fuel usage as well as delayed waste pickup times. The proposed framework builds a Deep Reinforcement Learning (DRL)-based system for dynamic waste collection route optimization using Autonomous Waste Collection Vehicles (AWCVs) with Internet of Things (IoT)-based smart bins. The system uses Deep Q-Networks (DQNs) to analyze traffic conditions data alongside vehicle battery level data with smart bin information from the system so vehicles carry out efficient waste collection operations using minimal energy. The newly proposed algorithm provides 30% distance reduction and 28% faster responses and 30% lower energy usage than baseline heuristic methods do. The conclusions show that AI-powered waste management strategies can create sustainable operations which increase both service performance and overall energy efficiency in cities. The research team plans to apply the proposed approach to actual waste collection systems while exploring methods of deploying multiple agents alongside renewable energy systems to enhance future urban waste management operations.

关键词： Waste management Schedules Renewable energy sources Smart cities Deep reinforcement learning Energy efficiency Real-time systems Vehicle dynamics Sustainable development Optimization

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Deep Learning Cerebral Splitting: Adaptation for Spine Elimination

Deep Learning Cerebral Splitting: Adaptation for Spine Elimi...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： M. Balasubramani B. J. Job Karuna Sagar K. C. Rajheshwari E. Saranya B. Aishwarya P. Murugeswari Assistant Professor Department of Computer Science and Engineering V.S.B. Engineering College Professor Department of Computer Science and Engineering Joginpally B.R Engineering College Hyderabad India Department of Computer Science and Engineering Sona College of Technology Assistant Professor Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Assistant Professor Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Department of Electronics and Communication Engineering Theni Kammavar Sangam College of Technology

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The posterior fossa volume and the total intracranial volume are important factors to consider when doing a brain volumetric analysis using structural electromagnetic resonance imaging. The thorough segmentation of the whole cortex offers a method for non-invasively evaluating different brain regions. In addition, the extraction of skulls in a way that maintains anonymity is increasingly being used as a means of disseminating large amounts of data obtained from brain images. Therefore, generalizing machine learning brain regions for intracranial measurements after skull removal is an interesting task. However, data collecting is difficult due to the small number of handwritten and whole-brain atlases marked with his TICV/PFV. In this study, we use U-Net tiles to estimate intracranial volume and perform whole-brain segmentation with and without cranial characteristics simultaneously. In the absence of individually recorded total cortical volume, a transfer learning approach has been shown to estimate extra her TICV and PFV labeling during whole-brain delineation on T1-weighted MRI. More specifically, U-Net tiles are pre-trained using a big Brain COLOR atlas generated using multi-atlas segmentation, and do not include his PFV labels. The pre-trained model is improved to learn more His TICV and PFV labels using the limited Brain COLOR atlas. We also elaborate on his method for processing MR images of the brain in the absence of a skull. The results show that our method produces reasonable segmentation and size estimation results in terms of average contact distance, cubic similarity index, and total volumetric similarity for both brains within and outside the skull. is showing off. This technology is currently available as open source to the general public.

关键词： Deep learning Image segmentation Image color analysis Magnetic resonance imaging Transfer learning Pipelines Estimation Skull Resonance Telecommunication computing

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Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation

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Procedia computer Science 2024年 233卷 254-268页

作者： N. Mathavan Dr. S. Sivaranjani Assistant Professor Department of Electronics &Communication Engineering Nadar Saraswathi College of Engineering and Technology Vadapudupatti Theni Tamil Nadu India Professor Department of Computer Science and Engineering Sethu Institute of Technology Kariyapatti Virudhunagar Tamil Nadu India

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy; (iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made; and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques .

关键词： Radio Frequency Identification (RFID) Received Signal Strength Indication (RSSI) localization tracking moving target virtual reference tag Spider Monkey Optimization (SMO) Artificial Neural Network (ANN) Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) tracking error tracking accuracy

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Cyberbullying Detection in Group Chat Applications- A Review

Cyberbullying Detection in Group Chat Applications- A Review

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： Rakhi Bhardwaj Ayush Billade Srinivas Chenna Atharva Deshpande Vaishnavi Arthamwar Assistant Professor Department of Computer Engineering Vishwakarma Institute of Technology Pune India Computer Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Cyberbullying in group chat applications has become an increasingly pressing issue in today's digital society, affecting users across various platforms. This review aims to explore and analyze the current techniques, methodologies, and challenges in detecting cyberbullying in group chat environments. By examining existing literature and approaches, we focus on how NLP, ML, and DL techniques are applied to identify harmful behaviors in real-time conversations. The review also highlights the complexities of analyzing unstructured text data, the role of sentiment analysis, and the need for context-aware models to detect subtle forms of harassment. Additionally, we investigate privacy concerns, ethical considerations, and the limitations of existing models in multilingual and culturally diverse groups. The paper emphasizes the importance of developing more robust, scalable, and adaptive detection systems to create safer online environments. This review concludes with future research directions, suggesting the integration of advanced AI techniques and collaborative efforts between researchers, developers, and policymakers to mitigate the growing cyberbullying problem in group chat applications.

关键词： Adaptation models Sentiment analysis Ethics Reviews Cyberbullying Oral communication Detectors Multilingual Complexity theory Context modeling

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

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Procedia computer Science 2023年 230卷 955-963页

作者： M.S. Geetha Devasana V. Krishna Kumar R. Selvaraj T. Satheesh Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore-641022 India Assistant Professor Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore-641048 India Assistant Professor Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore-641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

关键词： Machine Learning Generative Adversarial Network (GAN) Text to Image and Natural Language Processing

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Predicting Malware Classification and Family using Machine Learning: A Cuckoo Environment Approach with Automated Feature Selection

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Procedia computer Science 2024年 235卷 2434-2451页

作者： Niveditha S Prianka Rr Sathya K Shreyanth S Nandhagopal Subramani Balakrishnan Deivasigamani Karthikeyan S Department of Biotechnology Rajalakshmi Engineering College Thandalam Chennai Tamil Nadu India 602105 Senior Assistant Professor Department of AIML New Horizon College of Engineering Bengaluru Karnataka India 560103 Assistant Professor senior grade-1 School of Computer Science and Engineering VIT Vellore Tamilnadu India 632014 MTech in Data Science and Engineering Birla Institute of Technology and Science Pilani Rajasthan India 333031 Assistant Professor Department of CSE(AI and ML) KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India 641407 Assistant Professor Department of Information Technology KSR Institute for Engineering and Technology

Malware’s increasing menace in the digital realm needs the development of powerful detection and classification systems. This study presents a unique method for predicting malware category and family using machine learning, leveraging the Cuckoo environment and automated feature selection. To undertake an exhaustive examination of malware activity, we combined the Cuckoo environment with cutting-edge machine learning methods, such as the k-Nearest Neighbors (KNN) technique. The dynamic analysis of malware samples within the Cuckoo environment, which captured their interactions with the execution environment, provided remarkable insights into their malicious behaviors. The Boruta method aided automated feature selection, improving the feature set and optimizing model performance. A comparison with existing models yielded striking findings. Notably, Test Case 3 surpassed earlier cases by incorporating Automated Feature Selection + Cross-Validation. In identifying phishing attempts, it displayed outstanding specificity (90%), precision (93%), recall (96%), and an impressive F1-Score (92%), indicating its proficiency in reliably recognizing this frequent threat. Test Case 3 showed considerable increases as well, with a surprising 7.0% increase in precision and a notable 14.6% increase in recall when compared to Test Case 1. Furthermore, in the field of ransomware detection, Test Case 4, which solely focused on Automated Feature Selection, obtained excellent results, with 91% specificity, 92% accuracy, and a recall rate of 93%. These developments highlight the importance of the Boruta algorithm in optimizing the model’s performance.

关键词： Malware Classification Malware Family Prediction Machine Learning (ML) Random Forest Classifier (RFC) Cuckoo Environment Cybersecurity k-Nearest Neighbors (KNN) Boruta Algorithm Automated Feature Selection

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