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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Sharma, Nandini Singh, Shiv Shanker Khenwar, Medha Gupta, Priyanka Gla University Department of Computer Engineering and Application Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Smart farming technology allows farmers to control and analyse variables in real-time, helping them optimize crop growth and manage their operations more efficiently. The use of specialized sensors, data collection tools, and advanced analytics in Internet of Things (IoT) systems is helping to bridge the gap between physical farm conditions and digital monitoring. In this study, we aim to develop a system that monitors soil conditions to support smart farming. The system provides farmers with live data on key environmental factors, such as temperature, humidity, soil moisture, and soil temperature, to improve crop yield and product quality. The IoT setup for this intelligent farming solution includes components like the DHT11 and humidity sensors, a DS18B20 temperature probe, LDR, water pump, and a 12V LED strip, all controlled by a NodeMCU. When the system is activated, it continuously monitors soil and environmental conditions and sends this information to the cloud for real-time access. If the soil moisture falls below a set threshold, the system automatically triggers irrigation. Our experimental results show that this model improves decision-making for managing farming conditions. © 2024 IEEE.

关键词： Smart agriculture

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Secure Image Retrieval in an Untrusted Cloud Environment Using Homomorphic and Attribute-Based Encryption 3rd

Secure Image Retrieval in an Untrusted Cloud Environment Usi...

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Prakash, Vijay Nanda, Nandini Tiwari, Akriti Dhawan, Budh Singh, Pardeep Garg, Lalit School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Graphics Era Hill University Dehradun India Faculty of ICT University of Malta Msida Malta

ISBN: (纸本)9789819748594

The increasing popularity of cloud computing has led to a growing demand for efficient and secure data storage and retrieval. This is particularly challenging when dealing with image data, which often contains sensitive information that must be protected to ensure privacy and confidentiality. This paper proposes a framework for secure image retrieval in an untrusted cloud environment. A combination of cryptographic techniques and access control policies has been used to address the challenges of secure image retrieval. Specifically, homomorphic encryption enables the secure computation of image features without compromising the privacy of the underlying data. Additionally, we incorporate a novel access control mechanism based on attribute-based encryption (ABE), which allows for fine-grained access control based on user attributes such as job title or department. The proposed framework has a wide range of applications in the fields such as health care, finance, and government, where the need for secure storage and retrieval of image data is critical. By leveraging homomorphic encryption and ABE, the proposed framework provides strong protection against unauthorized access while maintaining high levels of flexibility and scalability. The potential consequences of unauthorized access to sensitive image data can be severe, ranging from personal privacy violations to financial fraud and identity theft. To evaluate the performance of our system, we conduct extensive simulations and achieve high levels of security and efficiency in retrieving image data from an untrusted cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Differential privacy

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Spine Disease Detection in Spinal X-Ray Images using Machine Learning 13

Spine Disease Detection in Spinal X-Ray Images using Machine...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving 100

LSM: A Comprehensive Metric for Assessing the Safety of Lane...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Gamerdinger, Jorg Teufel, Sven Amann, Stephan Volk, Georg Bringmann, Oliver University of Tübingen Faculty of Science Embedded Systems Group Department of Computer Science Germany

ISBN: (纸本)9798331517786

Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approaches consider an advanced safety evaluation of object detection, the evaluation of lane detection still lacks sufficient safety metrics. For assessing safety, additional factors such as the semantics of the scene with road type and road width, the detection range as well as the potential causes of missing detections, incorporated by vehicle speed, should be considered for the evaluation of lane detection. Therefore, we propose the Lane Safety Metric (LSM), which takes these factors into account in order to evaluate the safety of lane detection systems by determining an easily interpretable safety score. We evaluate our offline safety metric on various virtual scenarios using different lane detection approaches and compare it with state-of-the-art performance metrics. © 2024 IEEE.

关键词： Vehicle safety

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Machine Learning and Artificial Intelligence in Cybersecurity: Innovations and Challenges 2

Machine Learning and Artificial Intelligence in Cybersecurit...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Khatri, Vinayak Agarwal, Gaurav Gupta, Amit Kumar Sanghi, Akash Invertis University Department of Computer Science and Engineering Bareilly India KIET Group of Institutions Department of Computer Applications Ghaziabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries. © 2024 IEEE.

关键词： Phishing

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Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

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computers, Materials & Continua 2024年第1期78卷 795-810页

作者： Ala’a R.Al-Shamasneh Rabha W.Ibrahim Department of Computer Science College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Science and Mathematics Lebanese American UniversityBeirut11022801Lebanon Department of Mathematics Mathematics Research CenterNear East UniversityNicosia99138Turkey Information and Communication Technology Research Group Scientific Research CenterAlayen UniversityDhi Qar64001Iraq

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research *** of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into *** were made to mitigate the effects of image splicing,which continues to be a significant research *** study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep *** stages make up the proposed *** first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced *** proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image *** proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of sciences,Institute of Automation”which is a publicly available dataset for forgery *** experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and ***,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature *** the regions or locations where image tampering has taken place is limited by the *** research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

关键词： Image forgery image splicing deep learning Sonine functions

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Innovative Educational group Recommendation with Knowledge-Aware Attentive Embedding Learning

Innovative Educational Group Recommendation with Knowledge-A...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Singh, Mahesh Kumar Bhardwaj, Mukesh Kumar Sharma, Nishita Dwivedi, Shudhanshu Ranjan Yadav, Prerna Mishra, Divyanshi Department of Computer Science and Engineering Dronacharya Group of Institutions Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Dronacharya Group of Institutions Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

This research presents an innovative method for education group recommendation systems by leveraging Knowledge-Aware Attentive Embedding Learning (KA-AEL), aimed at enhancing the collaborative learning experience. The core innovation lies in integrating domain-specific knowledge into the recommendation process, thereby tailoring educational content to both individual and group learning needs. The KA-AEL framework utilizes an attentive embedding mechanism that dynamically adjusts the influence of various knowledge aspects based on their relevance to the learners' current objectives and comprehension levels. The methodology encompasses two key phases: knowledge-aware embedding, which encodes both content and user profiles with domain knowledge infusion, and an attentive group recommendation engine that predicts optimal learning paths by assessing group compatibility and individual learning preferences in unison. This dual-phase approach enables the system to discern and adapt to the evolving educational goals of diverse groups, promoting a more engaged and effective learning experience. © 2024 IEEE.

关键词： Collaborative learning

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A Comprehensive Analysis of Techniques and Applications in Multimodal Deep Learning 1

A Comprehensive Analysis of Techniques and Applications in M...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Tomar, Vivek Sharma, Swati Arora, Sangeeta Tyagi, Aditya Dayal University Computer Science & Engineering Department Dehradun India KIET Group of Institutions Delhi-NCR Computer Science & Engineering Department Ghaziabad India KIET Group of Institutions Delhi-NCR Computer Applications Department Ghaziabad India Sharda University CSA Department Greater Noida India

ISBN: (纸本)9798350353648

In recent years, deep learning has gained popularity and been used in many different applications. Models that can process and link data utilizing several modalities are the aim of multimodal deep learning (MMDL). Even with all the advancements made, unimodal learning is still unable to fully capture the entirety of human learning. Multimodal learning facilitates a better understanding and analysis of situations in which information is processed through multiple senses. The various modalities covered in this article include picture, video, text, audio, flow, RGB, stance, depth, face expressions, body motions, and point clouds. There has been a thorough examination of the most current developments in multimodal deep learning applications in the last few years, as well as a detailed investigation of the baseline methodologies. A detailed taxonomy of distinct multimodal deep learning techniques is presented, providing further details on specific applications. Finally, key issues are outlined for each domain separately, along with potential avenues for further research. © 2024 IEEE.

关键词： Artificial Intelligence Deep Learning Machine Learning Multimodal Natural Language Processing (NLP) UniModal

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Research on the behaviour of Bluetooth Low Energy protocol in the heart rate monitoring application 27

Research on the behaviour of Bluetooth Low Energy protocol i...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Balas, Zuzanna Tokarz, Krzysztof Zielinski, Bartlomiej Guzniczak, Tomasz Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland

In the paper, the authors check the behaviour of Bluetooth Low Energy protocol in a popular smart wristband and a microcontroller in the heart rate monitoring application. The measurements were collected using a development board with the ESP32 System on Chip. The authors tested measurement period stability and measurement reliability in various conditions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bluetooth Low Energy Health monitoring Internet of Things Smart wristband Wireless data transmission

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Domain-specific data characteristics: A study on meaning of stylometric sub-concepts and in-class imbalance 28th

Domain-specific data characteristics: A study on meaning of ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

In the context of data imbalance probably the most investigated problem is imbalance of classes, as learning from the data with this characteristic makes detection of existing patterns for all classes more difficult. However, other problems related to imbalance also exists and the paper addresses such cases where classes are balanced, but there is in-class imbalance. Such imbalance can be caused by uneven representation of sub-concepts. When there is a noticeable difference between the numbers of samples belonging to sub-concepts, this can turn the under-represented sub-concepts into disjuncts. Data irregularities of this type can hinder recognition, therefore actions are typically taken to restore balance. In the investigations described, the issue was studied in the stylometric domain and various classifiers were applied to the data that was balanced, then imbalanced, and finally with restored balance. The experiments show that the specifics of the domain of application can put its own mark on the data which is difficult to overcome by standard processing such as under- or oversampling. Observed dependence on a learner and dataset makes the issue even more complex and layered, and shows the need for deeper studies. © 2024 The Authors.

关键词： Data assimilation

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