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

作者： Sneha, A. Angel Diwan, B. Stella Maris College Of Engineering Artificial Intelligence and Data Science Department Nagercoil India Joesphs College Of Engineering Department of Computer Science St Chennai India

ISBN: (纸本)9798350370249

Millibots, miniature robotic platforms, have emerged as pivotal tools in various domains, ranging from medical interventions to environmental monitoring. However, their diminutive size presents formidable challenges in achieving autonomous navigation and control. In this paper, we delve into the realm of millibots and propose a groundbreaking approach to tackle these challenges by integrating deep learning algorithms for autonomous control. Leveraging the power of deep learning, we address the intricate interplay between perception, decision-making, and locomotion in small-scale robotics. Our methodology not only overcomes the limitations inherent to millibots but also unlocks new frontiers in their application. Through a comprehensive review of related work, meticulous experimentation, and insightful analysis, we demonstrate the unparalleled efficacy of our approach. This paper not only accelerates the advancement of small-scale robotics but also envisions a new era of autonomous exploration and innovation. © 2024 IEEE.

关键词： Motion planning

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Early Detection and Classification of Tomato Leaf Blight Disease Using Deep Neural Networks 2

Early Detection and Classification of Tomato Leaf Blight Dis...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Halder, Anangsha Guha Neogi, Subhrendu Dutta, Shibdas Khamaru, Koustov Bhattacharya, Arghya Roy, Shovan The Neotia University Department of Computer Science and Engineering Sarisha India Dcg Data-Core System India Department of Information Technology Kolkata India University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350373110

The world population relies on agricultural plants for food, and illnesses reduce yield, but proper plant disease monitoring can help to resolve such issues. computer vision and machine learning methods can detect plant illnesses early, reducing their negative consequences and overcoming the drawbacks of continual human monitoring. After pre-processing of input images, the target region needs to be isolated. The proposed models analyzes images for detection and classification to identify edges, colors, and textures. This research has agricultural applications for early disease identification. This study examined ResNet, MobileNet, DenseNet, InceptionV3 and modified CNN model on the PlantVillage dataset to classify tomato illnesses. The F1 score, accuracy, sensitivity, and specificity can evaluate models. This paper evaluates these models using a diverse collection of tomato leaf pictures, contributing to automated disease detection systems and may be helpful for improving farm management and sustainability. © 2024 IEEE.

关键词： Fruits

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Leveraging Neo4j for data science: Evaluating Traversal Efficiency in GDS and APOC for Directed Acyclic Graphs 7

Leveraging Neo4j for Data Science: Evaluating Traversal Effi...

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7th International Conference on data science and Information Technology, DSIT 2024

作者： Shatnawi, Hazim Saquer, Jamil The George Washington University Data Science Department WashingtonDC United States Missouri State University Computer Science Department SpringfieldMO United States

ISBN: (纸本)9798350384093

This paper presents a benchmark study of Breadth-First Search (BFS) and Depth-First Search (DFS) traversal algorithms applied to complex Directed Acyclic Graphs (DAGs) within Neo4j, utilizing the Graph data science (GDS) and Awesome Procedures on Cypher (APOC) libraries. DAGs are widely used in fields like data science, project management, software engineering, and bioinformatics to manage dependencies without cycles. Our experiments evaluate the performance of GDS and APOC on DAGs generated from Feature Models representing dependencies in Software Product Lines (SPL). Results indicate that GDS consistently outperforms APOC, particularly for large and intricate graph structures. These findings highlight the importance of optimized traversal techniques for managing complex DAGs efficiently, offering insights into scalability and performance improvements for real-world applications. © 2024 IEEE.

关键词： Directed graphs

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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data-Efficient Radiology Report Generation via Similar Report Features Enhancement 3rd

Data-Efficient Radiology Report Generation via Similar Repo...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Li, Yanfeng Sun, Jinghan Wang, Liansheng Department of Computer Science at School of Informatics Xiamen University Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (纸本)9783031820069

The utilization of Artificial Intelligence in automatically generating radiology reports presents a promising solution for enhancing the efficiency of the diagnostic process and reducing human error. However, existing methods require training on large datasets of image-report pairs, which are often scarce. Moreover, the accuracy of reports generated with limited paired data significantly diminishes. To address these challenges, this study introduces a data-efficient method that integrates the retrieval of similar reports with text fusion enhancements to tackle the scarcity of image-report pairs and generate accurate radiology reports. Our method is compared with several state-of-the-art approaches, showing advancements on the MIMIC-CXR and IU X-ray benchmarks with the same limited data pairs. It achieves near-optimal results on MIMIC-CXR and comparable results on IU-Xray, highlighting not only its effectiveness and potential to improve radiological diagnosis with fewer image reports but also its ability to generate more accurate reports. By enhancing cross-modal feature interaction and demonstrating higher diagnostic accuracy, this work contributes to the fields of clinical medicine and artificial intelligence. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Radiology

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Managing Vulnerabilities in Software Projects: the Case of NTT data 49

Managing Vulnerabilities in Software Projects: the Case of N...

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49th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2023

作者： Nocera, Sabato Romano, Simone Francese, Rita Burlon, Riccardo Scanniello, Giuseppe University of Salerno Department of Computer Science Fisciano Italy Ntt Data Salerno Italy

ISBN: (纸本)9798350342352

Background: Software vulnerabilities are flaws in application source code that can be exploited to cause harm, hence companies must devise strategies to manage ***: We want to understand how software vulnerabilities are managed in a big IT (Information Technology) service and consulting company like NTT ***: We conducted a focus group involving six software professionals working at NTT data and analyzed the gathered data through a thematic analysis ***: We found that application security standards are defined based on the needs of the clients (i.e., companies that commissioned NTT data the software to be developed) and the projects' nature (i.e., the development of greenfield projects vs. maintenance of existing ones). Also, to detect software vulnerabilities, SAST (Static Application Security Testing) tools are mainly used;among these, SonarLint and SonarQube appear to be the de-facto standards for NTT data. Finally, not all software vulnerabilities are fixed;for example, the presence of some software vulnerabilities is tolerated by the clients, who take on the responsibility of not removing these ***: It seems that developers and NTT data clients are not averse to securing their code. NTT data follows the application security standards established with their clients. To detect software vulnerabilities, SonarLint and SonarQube appear to be the de-facto standards, so explaining to some extent the increasing attention on these tools by the software engineering research community. © 2023 IEEE.

关键词： Software testing

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Application of Machine Learning for Ensuring Water Quality in Aquaculture Ponds

Application of Machine Learning for Ensuring Water Quality i...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Bharathi, S.V.Shri Sai, Paidipati Lakshman Ayyappa, Paidipati Charan Ram Reddy, Kakuru Jaya Praveen Reddy, Gangireddy Sai Preetham Rajesh, Katuri Madhava SRMIST- Ktr Department Of Data Science And Business Systems Tamil Nadu Chennai India Vit Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350352689

Aquaculture plays a pivotal role in meeting the growing global demand for seafood. However, ensuring optimal water quality within aquaculture ponds is a pressing challenge. This project proposes a paradigm shift by integrating machine learning to enhance water quality assurance. The system leverages real-time monitoring, predictive analytics, and adaptable algorithms, contributing to sustainability and responsible aquaculture practices. © 2024 IEEE.

关键词： Water quality

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Machine Learning based Wild-Animal Detection near Roads using IoT Sensor and Optimization

Machine Learning based Wild-Animal Detection near Roads usin...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

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Hate Speech Detection Using Enhanced Recurrent Neural Network Based on Elephant Herding Optimization on Social Media 3

Hate Speech Detection Using Enhanced Recurrent Neural Networ...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Senthilkumar, K. Sathiyabama, S. Ayyamuthukumar, D. Thiruvalluvar Government Arts College Department of Computer Science Namakkal Rasipuram India Department of Artificial Intelligence and Data Science Muthayammal College of Engineering Rasipuram India

ISBN: (纸本)9798350369083

In the past ten years, there has been a rise in nasty behaviors on social media due to the increased use of these platforms. One of the most offensive of these behaviors is hate speech, so users must safeguard themselves against it on social media. This research presents an approach to detect hate speech from social media using an optimized Elman recurrent neural network (ERNN) based on the hybrid local search algorithm (LSA) and elephant herding optimization (EHO) algorithm called LSAEHO. The LSA-EHO algorithm is used to LSA to obtain the optimal solution of EHO, enhance the convergence rate, and avoid the local optima problem of EHO. The LSA-EHO is used to find appropriate hyperparameters for ERNN to enhance the convergence rate and accuracy. The experimental results confirmed that the developed hate speech prediction-based optimized ERNN produced higher prediction accuracy and a faster convergence rate. © 2024 IEEE.

关键词： Elman neural networks

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Homomorphic Encryption and its Applications in Multi-Cloud Security 7

Homomorphic Encryption and its Applications in Multi-Cloud S...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kamble, Aahash Jiet, Moses Makuei Puri, Chetan Datta Meghe Institute of Higher Education And Research Department of Artificial Intellegence And Data Science Wardha India Department of Computer Science And Design Maharashtra Wardha India

ISBN: (纸本)9798350359299

This research study investigates the use of homomorphic encryption to bolster data security in multi-cloud environments. As cloud computing gains popularity, safeguarding data confidentiality, integrity, and privacy becomes paramount. Homomorphic encryption presents a solution by permitting calculations on encrypted data without decryption, thereby preserving data confidentiality and privacy. This paper delves into Partially Homomorphic Encryption (PHE) and fully homomorphic encryption (FHE), highlighting their capabilities and applications. The paper explores the difficulties of employing homomorphic encryption in systems that utilize multiple clouds. These difficulties include high computational costs, complex key management, issues with interoperability, and trouble identifying suitable applications. The paper emphasizes the need for continuing research and cooperation among academic institutions, businesses, and governmental organizations to address these difficulties and maximize the potential of homomorphic encryption in environments with multiple clouds. © 2024 IEEE.

关键词： Cryptography

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