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Critical Review of Cybersecurity Risks in Precision Agriculture 2

Critical Review of Cybersecurity Risks in Precision Agricult...

引用

2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Kumar, Shubham Faisal, Mohammad Integral University Dept. of Computer Application Lucknow India

ISBN: (纸本)9798350364699

Traditional and smart agricultural knowledge are covering climate, soil health, and human health, remains crucial for many regions. Small farmers, who manage most of the world's farms and especially rely on it. Larger farms also benefit by blending traditional and modern practices. Integrating precision agriculture (PA) techniques such as IoT, ML, and AI to enhances farming productivity and sustainability. These Integrated PA techniques have emerged as transformative approaches. In this paper explores the benefits and challenges of PA that emphasizing its potential to revolutionize farming practices through real-time monitoring and predictive analytics. However, these integration PA techniques introduces significant cybersecurity risks, financial risk, and technological risk, which necessitate robust security measures to protect sensitive agriculture information. This study reviews the current state of PA, identifies different key risks, and examines various research contributions toward addressing security challenges. The major findings highlight the importance of ensuring data integrity, protecting privacy, and implementing advanced security solutions to foster trust and enhance the efficiency of PA systems. © 2024 IEEE.

关键词： Smart agriculture

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Progressively Stacked Serial Encoder Decoder for MRI Super Resolution

Progressively Stacked Serial Encoder Decoder for MRI Super R...

引用

2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Tulasiram, Jinaga Dutta, Shoumik Banothu, Balaji Nickolas, S. NIT Tiruchirappalli Dept. of Computer Application Tiruchirappalli India

ISBN: (纸本)9798350376135

Low-resolution MRI images in the through-plane direction impose constraints on both time and cost. This insufficient level of resolution does not meet the requirements for high-resolution imaging that is necessary for early diagnosis of brain diseases and morphometric analysis. Conventional single-image super-resolution (SISR) techniques have significant obstacles, such as effectively integrating intricate local details with the overall anatomical structure. In order to address these difficulties, we propose a novel technique known as the Progressively Serial-Stacked Encoder Decoder (PSSED) to improve the resolution of brain MRI scans. This approach employs convolutional blocks to extract specific information and enhance the quality of the image. Our strategy has been proven to be superior to previous Single Image Super-Resolution (SISR) methods based on extensive experimentation using publically available datasets. Our findings highlight the potential of deep learning techniques to address the constraints of low-resolution MRI images, paving the path for improved diagnostic accuracy and lower radiation exposure in clinical practice. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Automated Landslide Detection using Ensemble Learning

Automated Landslide Detection using Ensemble Learning

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Tanishka Agrawal, Subhash GLA University Dept. of Computer Engineering & Application Mathura India

ISBN: (纸本)9798350378092

Landslides pose significant threats to human life, infrastructure, and the environment, making timely detection crucial for effective disaster management. This research paper introduces a novel approach to landslide detection utilizing Convolutional Neural Networks (CNNs) in conjunction with ensemble learning techniques. Leveraging the inherent capabilities of CNNs to extract discriminative features from remote sensing imagery, multiple CNN models are trained and aggregated into an ensemble. Through extensive experimentation on diverse satellite imagery datasets, the effectiveness of the ensemble model is evaluated against standalone CNNs and traditional methods. Results demonstrate that the ensemble approach outperforms individual CNN models and baseline methods regarding accuracy and robustness across varying environmental conditions, enhancing landslide detection capabilities and contributing to more effective disaster management strategies. Our Model achieves 95% Precision, 91% Recall, and 93% f1-score which makes it better than all comparative methods. After classification, we make predictions through GUI by uploading images, in which we are using the Nepal dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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From Pixels to Pain: The Silent Health Risk Associated with Electronic Device Use 8

From Pixels to Pain: The Silent Health Risk Associated with ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Alamma, B.H. Smitha, G.V. Khaiyum, Samitha Sanchita Dayananda Sager College of Engineering Dept of Master of Computer Application Bangalore India

ISBN: (纸本)9798331505462

In the field of artificial intelligence (AI), machine learning is a subset that focuses on using data to learn and create prediction algorithms. Electronics controls the flow of electricity for processing, and health entails more than just being free from disease. On the other hand, overusing devices can increase health hazards. The inquiry consists of an empirical study that was carried out in Istanbul, Turkey, with 1147 students, as well as a systematic evaluation of ML applications in mental health within the human-computer interface (HCI) literature. The condition of emotional, psychological, and social well-being that affects a person's thoughts, feelings, and behaviour in day-to-day living is known as mental health. The study examines how teens' mental and physical health are affected by their excessive use of digital gadgets, especially cellphones. In this artical the integration of digital technologies and machine learning (ML) in relation to teenage wellbeing and mental health treatment is examined. The assessment also emphasises the necessity of humancentered and morally sound machine learning applications in mental health services. It also discusses how machine learning (ML) might improve mental health services by improving the identification, diagnosis, and treatment of mental health issues. The results show that teens' psychiatric problems, musculoskeletal discomfort, and sleep disruptions are linked to excessive digital device useThe study comes to the conclusion that while ML and digital technologies have a great deal of promise for enhancing mental health outcomes, there are also important socio-technical issues that require attention. These include worries about model bias, data privacy, and the generalisation of machine learning algorithms. Future studies should concentrate on creating rigorous assessment frameworks and transdisciplinary methods to guarantee the ethical and successful application of ML in mental health services. © 2024 IEEE.

关键词： Electronic health record

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Comparative Analysis of SVM, Decision Tree, Random Forest for Crop Recommendation 2

Comparative Analysis of SVM, Decision Tree, Random Forest fo...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Shende, Aryan Yeole, Shrijeet Kanakia, Harshil T. Salmani, Sakina Sardar Patel Institute of Technology Dept of Master of Computer Application Mumbai India

ISBN: (纸本)9798350327533

A sizable portion of Indians view agriculture as their primary source of income. The cultivation of crops is crucial to the economy. Crop production of poor quality is frequently the result of choosing the incorrect crops for the wrong soil or not knowing enough about the growth potential of various crops. The suggested method, which bases crop suggestion on machine learning, is based on measurements of soil properties that have been previously recorded This method helps to maintain crop health and reduces the likelihood of soil degradation. Numerous elements, among them rainfall. Temperature, pH, N, P, K, and humidity are all analyzed using machine learning techniques including random forests, decision trees, and support vector machines. Based on these analyses, recommendations are provided for cultivating an appropriate crop. © 2024 IEEE.

关键词： Decision Tree Random Forest Support Vector Machine

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

引用

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

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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Youtube Comments Sentiment Analysis 9

Youtube Comments Sentiment Analysis

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Giri, Rushikesh Sirsath, Mihir Kanakia, Harshil T. Sardar Patel Institute of Technology Dept of Master of Computer Application Mumbai India

ISBN: (纸本)9798350394474

Sentiment analysis is a method used to identify and understand user opinions and viewpoints regarding a product or service. YouTube, one of the most popular video-sharing platforms, garners millions of views daily, resulting in a plethora of user comments that hold valuable information for improving video rankings. To extract sentiment from these comments, Machine learning techniques and natural language processing (NLP) are used.. Various attempts have been made to classify sentiment into two (positive or negative), three (positive, negative, and neutral), or multiple (e.g., happy, surprised, sad, angry) classes. This study classifies YouTube comments, investigates sentiment analysis techniques that can be used on them, and offers insightful information useful for data mining and sentiment analysis research © 2024 IEEE.

关键词： Sentiment analysis

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A Graph Based Approach For Detecting Sybil Node In Online Social Networks 15

A Graph Based Approach For Detecting Sybil Node In Online So...

引用

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

作者： Chandra, Sucheta Das, Priyanka Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350370249

Sybil attacks pose a significant threat to the security and integrity of online social networks by allowing malicious actors to create multiple fake identities to manipulate network behavior. The abstract outlines a method for detecting Sybil attacks in online social networks using graph analysis. It involves constructing a graph representation of the network, calculating node centrality measures such as in-degree and out-degree, and setting a degree threshold to identify potentially malicious nodes. Additional parameters like clustering coefficient and community structure are also considered to improve detection accuracy. The proposed approach aims to enhance the security of online social networks by identifying and isolating Sybil identities, thus preserving network integrity and trustworthiness. The authors' firmly assert that employing graph-based theory for Sybil node detection in online social networks stands out as one of the most effective methods. This approach not only offers robustness but also scalability, allowing for efficient detection even in large-scale networks. With its ability to unveil intricate relationships, anomalies, and patterns within the network, graph-based theory emerges as a cornerstone in the fight against Sybil attacks. © 2024 IEEE.

关键词： Cyber attacks

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Utilizing Deep Learning Techniques for Categorizing Architectural Heritage Images 2

Utilizing Deep Learning Techniques for Categorizing Architec...

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

作者： Pujari, Laxmi Ganamukhi, Pratibha Adur, Sindhu S. Dept. of Master of Computer Application KLE Institute of Technology Hubballi India

ISBN: (纸本)9798350383867

In this research, we address the challenge of manually categorizing architectural heritage images, which is time-consuming and subjective. We propose an automated image classification method using Python and machine learning, specifically deep learning. The system recognizes various architectural features like altars, apses, bell towers, columns, domes, flying buttresses, gargoyles, stained glass, and vaults. Our approach employs a deep learning model trained on annotated images of buildings. Users can upload architectural photos, which are then scaled, converted into a numpy array, and fed into the deep neural network for prediction. The model predicts the architectural category and provides the likelihood of the prediction. The system displays the predicted category together with the actual image, prediction accuracy, and a description of the identified class. Additionally, a bar graph visualizes the class probabilities for better interpretation. The dataset includes 10 categories split into training and testing folders, comprising 5937 and 1434 images, respectively. The evaluation results indicate that DenseNet121 achieved 86.61% accuracy, ResNet50v2 reached 84.24%, and MobileNetv2 achieved 74%. © 2024 IEEE.

关键词： Stained glass

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