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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 IEEE.

关键词： k-nearest neighbors

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Data delivery delay and cross-layer packet size analysis for reliable transmission of Licklider transmission protocol in space networks

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science China(Information sciences) 2024年第9期67卷 229-244页

作者： Guannan YANG Ruhai WANG Kanglian ZHAO Wenfeng LI Dong YAN School of Information Engineering Nanjing University of Finance and Economics Drayer Department of Electrical and Computer Engineering Lamar University School of Electronic Science and Engineering Nanjing University Beijing Aircraft Overall Design Department

Delay/disruption tolerant networking(DTN) is proposed as a networking architecture to overcome challenging space communication characteristics for reliable data transmission service in presence of long propagation delays and/or lengthy link disruptions. Bundle protocol(BP) and Licklider Transmission Protocol(LTP) are the main key technologies for DTN. LTP red transmission offers a reliable transmission mechanism for space networks. One of the key metrics used to measure the performance of LTP in space applications is the end-to-end data delivery delay, which is influenced by factors such as the quality of spatial channels and the size of cross-layer packets. In this paper, an end-to-end reliable data delivery delay model of LTP red transmission is proposed using a roulette wheel algorithm, and the roulette wheel algorithm is more in line with the typical random characteristics in space networks. The proposed models are validated through real data transmission experiments on a semi-physical testing platform. Furthermore, the impact of cross-layer packet size on the performance of LTP reliable transmission is analyzed, with a focus on bundle size, block size, and segment size. The analysis and study results presented in this paper offer valuable contributions towards enhancing the reliability of LTP transmission in space communication scenarios.

关键词： Licklider transmission protocol DTN bundle protocol cross-layer packets size space communication

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In-service performance of emergency shutdown valves and dependent operational relationships in the offshore oil and gas industry

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Petroleum 2023年第4期9卷 613-620页

作者： Keith Cameron Andrew Lewis Diogo Montalvao Mohammad Reza Herfatmanesh School of Physics Engineering and Computer ScienceUniversity of HertfordshireUnited Kingdom Department of Design and Engineering Faculty of Science and TechnologyBournemouth UniversityUnited Kingdom

Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.

关键词： Emergency Shutdown Valve Safety instrumented systems Closure time IEC-61511

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Enhancing coordinated target tracking: innovative particle filters with meta-heuristic integration and advanced model validation

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Soft Computing 2025年第7期29卷 1-32页

作者： Das, Nitish Kulkarni, Nilima Department of Computer Science and Engineering MIT Art Design and Technology University Loni Kalbhor Pune412201 India

Coordinated target tracking and its augmented variant represent significant challenges in modern surveillance. These tasks are essential for accurately localizing and predicting the movement of dynamic targets. This article introduces an advanced particle filter algorithm with a packet dropout mechanism to enhance tracking accuracy. Additionally, it proposes three meta-heuristic-driven particle filters: the Particle Filter integrated with the Chaotic Gravitational Search Algorithm (PF-CGSA), the Particle Filter integrated with the Improved Grey Wolf Optimizer (PF-IGWO), and the Particle Filter integrated with the Imperialist Competitive Algorithm with Regional Domination Policy (PF-ICARD). These methods are tested using three sophisticated models: the Coordinated Turn Target Tracking Model (CT model), the Augmented Coordinated Turn Target Tracking Model with Cartesian Velocity (ACT1 model), and the Augmented Coordinated Turn Target Tracking Model with Polar Velocity (ACT2 model). Experimental results demonstrate that PF-CGSA, PF-IGWO, and PF-ICARD achieve higher convergence speeds and accuracy. They also effectively mitigate premature convergence phenomena. Comparative analysis based on Root Mean Squared Error (RMSE) metrics underscores their superiority in real-world target tracking challenges, highlighting the potential of these advanced particle filtering techniques in enhancing precision and efficiency in target tracking. Particularly, PF-ICARD achieves the highest accuracy, reducing RMSE by up to 18.64% compared to PF-CGSA and PF-IGWO, making it ideal for high-precision applications. PF-CGSA excels in specific scenarios, notably improving X-coordinate tracking in the ACT2 model by up to 11.39%. Meanwhile, PF-IGWO offers a balanced solution with moderate accuracy and enhanced computational efficiency, suitable for real-time applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Aircraft detection

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A Review: Transformative Impact of Data Visualization Across Various Industries 8

A Review: Transformative Impact of Data Visualization Across...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sharma, Mansi Puri, Chetan Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798350376425

In today's data-driven world, data visualization has emerged as a powerful tool for transforming raw data into actionable insights. This paper explores the profound impact of data visualization across various industries, including banking, healthcare, and education. By utilizing advanced visualization tools, professionals can gain unparalleled clarity in identifying patterns, trends, and hidden opportunities within complex datasets. The introduction of dynamic dashboards and immersive 3D representations are revolutionizing the process of data interpretation, enabling real-time monitoring and enhanced spatial understanding. This research study analyzes the transformative effects of data visualization on decision-making, particularly in the realm of finance and budgeting. Visual representations can uncover hidden patterns, mitigate risks, and inform strategic decisions. Beyond these industries, data visualization is also revolutionizing marketing, manufacturing, and education. By enhancing creativity, streamlining workflows, and improving communication, visual experiences are driving innovation and progress. Overall, data visualization is a critical tool for unlocking the value of data in today's information-intensive world. By providing actionable insights and driving effective decision-making, data visualization is transforming industries and empowering organizations to achieve their goals. © 2024 IEEE.

关键词： Budget control

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A Comprehensive Study of Water Quality Trends and Seasonal Variations in Coastal Bays 2

A Comprehensive Study of Water Quality Trends and Seasonal V...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Sharma, Mansi Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331528713

Monitoring and analysing water quality parameters is fundamental for the early detection of natural changes in coastal bays. This paper examines regular varieties and relationships among key water quality parameters - salinity, broken up oxygen, pH, Secchi profundity, water profundity, and water temperature - collected from a coastal bay over a period in 1994. Information preprocessing included changing over dates to a standardized arrange and guaranteeing numerical consistency over parameters. Exploratory data analysis (EDA) given experiences into the temporal patterns of individual parameters. Consequent relationship examination revealed significant interdependencies among the parameters, highlighting potential impacts of seasonal and environmental components. The paper moreover reviewed monthly midpoints to recognize regular designs. Line plots laid out the varieties in saltiness, broken up oxygen, pH, and water temperature over differing months, emphasizing the cyclic nature of these parameters. Comes about appear unmistakable regular designs, such as higher broken down oxygen levels amid colder months and assortments in saltiness comparing to potential freshwater convergences. These revelations emphasize the centrality of nonstop water quality watching to induce it the energetic natural forms impacting coastal ecosystems. This investigate contributes to natural administration methods by giving a comprehensive examination of water quality variances, helping inside the definition of approaches for the conservation and economical administration of coastal bay regions. © 2024 IEEE.

关键词： Abiotic

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A Comparative Analysis of Diabetes Prediction System Using Regressor Methods

A Comparative Analysis of Diabetes Prediction System Using R...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Mansi Kumar, Praveen Puri, Chetan Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331508432

Hyperglycaemia may be a trademark of diabetes mellitus, a chronic metabolic disease that has picked up universal consideration. The administration of diabetes and its related complications depends intensely on early recognizable proof and prevention. Routine approaches to diabetes conclusion can involve intrusive examinations and drawn-out methods. The objective of this development is to utilize machine learning procedures to make a solid and successful diabetes prediction system. A dataset comprising a few patient features, such as age, gender, BMI, affront and glucose levels, pregnancy history, and other related data, is utilized by the recommended framework. In arrange to suit lost values and standardize the information, certain characteristics are pre-processed. Predictive models are at that point prepared utilizing machine learning strategies with logistic regression, support vector machines, random forest, and gradient boosting. Metrics like as accuracy, precision, recall, F1-score, and zone beneath the bend (AUC) are utilized to assess the execution of the show. These measures are utilized to decide which show performs the finest and how well it generalizes to unused information. The system's user-friendly interface makes it simple to enter quiet data and get a anticipated diabetes determination. The ponder progresses the determination of diabetes by giving a non-invasive strategy which will be more exact. Way better early detection might lead to provoke mediation and made strides ailment administration in case the proposed approach is actualized. Additionally, it can reduce the toll that diabetes has on healthcare frameworks and offer assistance therapeutic professionals make well-informed choices. © 2024 IEEE.

关键词： Prediction models

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Predicting Heart Disease: A Machine Learning Approach to Cardiac Health 2

Predicting Heart Disease: A Machine Learning Approach to Car...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Sharma, Mansi Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331528713

Heart disease continues to be a major worldwide health problem, taking many lives annually. Reducing death rates and moving forward persistent results depend heavily on early recognizable proof and anticipation. Conventional techniques for diagnosing cardiac disease can be time-consuming and can require intrusive strategies. Machine learning has ended up a practical strategy for handling these issues in later a long time. Machine learning calculations are able of distinguishing patterns and making exceptionally exact forecasts almost the possibility of cardiac infection by assessing enormous databases of persistent data. This study looks into how machine learning may be utilized to estimate the hazard of heart infection depending on distinctive quiet characteristics. A huge dataset was utilized, which included details approximately blood weight, cholesterol, age, gender, smoking status, and the presence of certain medical disorders. Through the utilize of cutting-edge machine learning strategies such as Random Forest, Support Vector Machine, and Neural Networks, our goal was to make models that may dependably distinguish individuals as either tall or low chance heart malady patients. The comes about of this think about illustrate how machine learning has the capacity to totally change the field of heart illness anticipation. Healthcare experts can act early with focused interventions, such as pharmaceutical, lifestyle changes, or more advanced demonstrative strategies, by accurately recognizing those who are at high risk. This individualized approach to treatment can lower the overall burden of heart illness and enormously upgrade understanding results. This study appears how well machine learning can anticipate the hazard of heart infection. In specific, the Random Forest demonstrate turned out to be a valuable asset for deciding which individuals would benefit from early mediation. We may expect the advancement of continuously more complex and precise models as ma

关键词： Support vector machines

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A Deep Learning Approach to Classify Skin Cancer

A Deep Learning Approach to Classify Skin Cancer

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sharma, Mansi Kumar, Praveen Department of Computer Science and Design Wardha India Department of Computer Science and Medical Engineering Wardha India

ISBN: (纸本)9798331508432

A effective treatment for skin cancer depends on early discovery, which may be a major around the world health issue. This study offers a interesting strategy for dependably classifying skin injuries as benign or malignant by utilizing convolutional neural networks (CNNs). Convolutional Neural Networks (CNNs) are a promising strategy with incredible potential for skin cancer screening. CNNs are well-known for their capacity to observe complex designs from picture data and are able of accurately recognizing benign or malignant skin injuries. Analysts can make exceptionally valuable demonstrative devices by utilizing enormous datasets of skin injury photos to prepare CNN models. Methods like information augmentation, ensemble approaches, transfer learning, attention mechanisms, and generative ill-disposed systems can be utilized to make strides execution indeed encourage. Besides, making mobile and cloud-based apps and examining logical AI can progress the impact and openness of skin cancer diagnosis. CNNs are idealize for this reason because of their reputation for being able to extract complex patterns from visual information. Our approach involves employing a sizable dataset of photographs of skin injuries that have been meticulously labeled to prepare a CNN show. The demonstrate is planning to secure discriminative properties, such as asymmetry, uneven borders, color change, distance across, and creating characteristics, that distinguish benign from malignant tumors. The results of our tests appear how well our proposed CNN-based strategy works to distinguish skin cancer at high accuracy rates. The demonstrate ceaselessly beats out conventional machine learning approaches, illustrating the benefits of profound learning strategies in this field. This ponder progresses the field of dermatology's computer-aided conclusion, giving a possibly valuable instrument to assist therapeutic specialists distinguish and treat skin cancer early on. © 2024 IEEE.

关键词： Convolutional neural networks

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Intelligent Generation of Plant Landscaping on Bidirectional Recurrent Network Auto-encoder

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 474-491页

作者： Zhao, Yu-Hang Tang, Zhen He, Zhong-Jun School of Art and Design Shanghai University of Engineering Science Shanghai201620 China School of Computer Science Taylor’s University Selangor 47500 Malaysia

In the three-dimensional modeling technology, it is very difficult to draw all plants accurately and carefully. The current level of computer software and hardware is far from meeting the requirements. In addition, many application fields require realtime rendering, so we must make the necessary balance between accuracy and complexity. Therefore, in order to solve the above problems, a fast and realistic intelligent generation method of plant landscaping based on deep learning is proposed. Firstly, Bezier parametric curves are used as vertex control curves to generate trunks, branches and leaves, and various plant entities are generated by combining the diversity of geometric models with the diversity of image textures. Then, based on the half-edge folding algorithm, the low-precision three-dimensional geometric model is optimized by using the bidirectional cyclic network auto-encoder, so as to improve the fidelity and reduce the computational overhead. Finally, in the process of detail level drawing, a preprocessing selection stage is added, and the appropriate parallax measurement coefficient is selected for the scene area of each frame through the calculation of projection value. The experimental results show that compared with other modeling methods, the proposed modeling method realizes the rapid rendering of plants, and shows better performance in frame rate change and execution time. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Geometry

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