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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Optimizing University Time Table Schedules for Smart Space Utilization 5

Optimizing University Time Table Schedules for Smart Space U...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Dasari, Chetan Aditya Kumar, Solasa Manikanta Arpitalaxmi, Arpitalaxmi Thushara, M.G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350376685

Manual university course timetable scheduling is a time-consuming and error-prone process, often resulting in conflicts and inefficiencies. An optimized timetable is crucial for smooth academic operations, ensuring efficient use of resources and fostering a positive learning environment for students and faculty. We address the challenge of university course scheduling by proposing an optimized time-table scheduling algorithm. This paper will identify suitable approaches to automate timetable generation, considering factors like lecturer availability, student groups, room capacity, and course requirements. Keeping track of all the different factors that affect things like course requirements, teacher availability, room capacity, and fair task distribution is hard. By working on slot-based scheduling methods, we hope to find ways to make the schedule work best for both students and teachers, which will make everyone happier and help them learn more. © 2024 IEEE.

关键词： Students

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Investigating Natural Language Techniques for Accurate Noun and Verb Extraction 2

Investigating Natural Language Techniques for Accurate Noun ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Reshma P. Thushara, M.G. Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri India

Natural language processing (NLP) has witnessed significant advancements in recent decades. Automatically classifying parts of speech, like nouns and verbs, from textual input has transformed text analysis and language understanding. Using natural language processing techniques, we explore various methods for identifying noun and verb phrases automatically, with an emphasis on high accuracy. Our study explores rule-based, statistical, and Machine Learning (ML) approaches for determining the nouns and verbs from sentences. The effectiveness of these approaches is clearly evident, especially when NLP libraries such as SpaCy and the Natural Language Toolkit (NLTK) are used. As well as demonstrating their potential applications across diverse language processing tasks and industries, we conduct comparative research to showcase their advantages and disadvantages. The performance of these methods is also examined in terms of retrieving subject and action terms. SpaCy achieves an impressive accuracy of 95% in noun and verb extraction, while Part-Of-Speech (POS) technology tagging delivers an even higher accuracy of 96%. The results obtained with these methods illustrate how nouns, verbs, and names can be classified in text successfully. © 2024 Elsevier B.V.. All rights reserved.

关键词： Extraction

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Performance Evaluation of Drones in FANETs using Queueing Model

Performance Evaluation of Drones in FANETs using Queueing Mo...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Ghosh, Akash Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Pandey, Trilok Nath Pattanayak, Binod Kumar De, Utpal Chandra School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

When conventional communication routes are unavailable or malfunctioning, mobile adhoc networks (MANETs) are essential for establishing communication. These networks can be easily and cheaply built whenever necessary because they don't require any pre-existing infrastructure. In some extreme situations (such as floods, battle zones, rescue operations, etc.), MANETs cannot be used. Lacking a fixed infrastructure in some situations, flying ad hoc networks (FANETs) can be quite useful for establishing communication. In FANETs, many drones are connected one to another directly or through middle nodes. In this research work, the arrival rate of events during the catastrophe, the drone's service rate, the waiting buffer of the system is considered and various performance measures such as the waiting time of the requests in the waiting buffer, the waiting time of the requests in the system, average queue length and the drones' idle time have all been considered. The ideal number of drones should be used to handle catastrophic occurrences and reduce casualties by adopting the queuing model. © 2023 IEEE.

关键词： Catastrophe Drones Flying ad-hoc networks Performance Measures Queueing Model

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A Survey on Energy efficiency in Cloud Computing Frameworks at Different Platforms 5

A Survey on Energy efficiency in Cloud Computing Frameworks ...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Anul Solanki, Kamna Dhankar, Amita Uiet Maharishi Dayanand University Department Of Computer Science And Engineering Rohtak India Maharishi Dayanand University Department Of Computer Science And Applications Rohtak India

ISBN: (纸本)9798350330861

Cloud computing is a paradigm that offers a restricted set of virtualized resources that may be accessed as needed. It is crucial to allocate these resources on target machines in a manner that minimizes both inadequate utilization and excess utilization, as these conditions can result in substantial consumption of energy. The term 'nano data centers' refers to a network of widely dispersed computers that have the capability to both host and distribute material and solicitations in a peer-to-peer manner. The investigation of energy usage in nano servers has not been extensively explored, despite their increasing popularity. Several findings have been derived from our study, which highlight the aspects within the system design that enable nDCs to achieve lower energy consumption compared to their centralized counterparts. This paper presents a comprehensive definition of Cloud Frameworks, encompassing many platforms such as edge computing, green computing, parallel computing, distributed computing, and fog computing. In conclusion, this study highlights a number of obstacles and potential within the realm of cloud computing. It is our intention that this work will attract the interest of the academic community and serve as a catalyst for further investigation in this domain. © 2023 IEEE.

关键词： Fog computing

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Deep Learning Based Intrusion Detection for IoT Networks 4

Deep Learning Based Intrusion Detection for IoT Networks

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ananthi, P. Nirmaladevi, K. Mothika, G. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Erode India

ISBN: (纸本)9798331529635

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. The project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. The novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. The TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. The ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance. © 2024 IEEE.

关键词： Network intrusion

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A Survey of Machine Learning for Assessing and Estimating Student Performance 5th

A Survey of Machine Learning for Assessing and Estimating St...

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Kaur, Avneet Bhatia, Munish Department of Computer Science and Engineering Lovely Professional University Punjab India Department of Computer Applications NIT Kurukshetra Kurukshetra India

ISBN: (纸本)9789811998751

Educational data mining (EDM) contributes cutting-edge methodologies, strategies, and applications to the advancement of the education system, hence playing a crucial part in its development. Utilising machine learning and data mining approaches to explore and utilise educational data, the current advancement gives essential tools for comprehending the student learning environment. Academic institutions in the twenty-first century operate in a highly competitive and complicated environment. Among the prevalent issues faced by universities are performance analysis, the provision of a high-quality education, systems for evaluating the performance of students, and the planning of future activities. Student intervention programmes must be created in these universities in order to address the academic difficulties encountered by students. From 2009 through 2021, the relevant EDM literature relative to predicting student attrition and students at risk is examined in this review. According to the review’s results, several machine learning (ML) methodologies are used to discover and address the fundamental challenges of forecasting students at risk and student withdrawal rate. Furthermore, the bulk of studies make use of data from student college/university database and online learning portals. It was determined that ML techniques play crucial roles in forecasting students at risk and withdrawal rates, hence boosting student performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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IoT-Based Smart Mask Protection against the Waves of COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2023年第8期14卷 11153-11164页

作者： Goar, Vishal Sharma, Aditi Yadav, Nagendra Singh Chowdhury, Subrata Hu, Yu-Chen Govt. Engineering College Bikaner Rajasthan Bikaner India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India Department of Computer Science & Applications SVCET Engineering College Andra Pradesh Chittoor India Department of Computer Science and Information Management Providence University 200 Sec. 7 Boulevard Shalu Dist Taichung City43301 Taiwan

In the year 2020, the word "pandemic" has become quite popular. A pandemic is a disease that spreads over a wide geographical region. The massive outbreak of coronavirus popularly known as COVID-19 has halted normal life worldwide. On 11th March 2020, the World Health Organization (WHO) quoted the COVID-19 outbreak as a "Pandemic". The outbreak pattern differs widely across the globe based on the findings discovered so far;however, fever is a common and easily detectable symptom of COVID-19 and the new COVID strain. After the virus outbreak, thermal scanning is done using infrared thermometers in most public places to detect infected persons. It is time-consuming to track the body temperature of each person. Besides, close contact with infected persons can spread the virus from the infected persons to the individual performing the screening or vice-versa. In this research, we propose a device architecture capable of automatically detecting the coronavirus or new COVID strain from thermal images;the proposed architecture comprises a smart mask equipped with a thermal imaging system, which reduces human interactions. The thermal camera technology is integrated with the smart mask powered by the Internet of Things (IoT) to proactively monitor the screening procedure and obtain data based on real-time findings. Besides, the proposed system is fitted with facial recognition technology;therefore, it can also display personal information. It will automatically measure the temperature of each person who came into close contact with the infected humans or humans in public spaces, such as markets or offices. The new design is very useful in healthcare and could offer a solution to preventing the growth of the coronavirus. The presented work hasa key focus on the integration of advanced algorithms for the predictive analytics of parameters required for in-depth evaluations. The proposed work and the results are pretty effectual and performance cognizant for predictive analytic

关键词： COVID-19

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An Empirical Study of Deep Learning Techniques for Diabetic Retinopathy Detection 3

An Empirical Study of Deep Learning Techniques for Diabetic ...

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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Pandey, Trilok Nath Pattnaik, Shyam Sankalp Sharma, Sanyam Ravalekar, Vishvajeet Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar School of Computer Science and Engineering VIT Chennai India School of Computer Applications Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (纸本)9798350303360

Presently, a competent professional must perform a thorough manual examination of digital color fundus photographs of the retina in order to diagnose diabetic retinopathy (DR). The delayed results result in missed evaluation, inaccurate communication, and postponed therapy by the time technicians complete their evaluations, which is often a day or two later. By looking for lesions connected to the vascular anomalies brought on by the disease, doctors can spot DR. This strategy works well, but it uses a lot of resources. Where DR detection is most important and there is a high prevalence of diabetes in the area, there is typically a lack of the requisite information and resources. As the number of persons with diabetes increases, the infrastructure required to prevent DR-related blindness will become increasingly more insufficient. In order to diagnose and classify diabetic retinopathy, this paper aims to develop and deploy image processing and ML algorithms to analyze fundus images. © 2023 IEEE.

关键词： Deep learning

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MEMORY-ASSISTED SUB-PROTOTYPE MINING FOR UNIVERSAL DOMAIN ADAPTATION 12

MEMORY-ASSISTED SUB-PROTOTYPE MINING FOR UNIVERSAL DOMAIN AD...

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12th International Conference on Learning Representations, ICLR 2024

作者： Lai, Yuxiang Zhou, Yi Liu, Xinghong Zhou, Tao School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China School of Computer Science and Engineering Nanjing University of Science and Technology China

Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as the unknown class during the adaptation process, as it is not included in the source domain. However, most existing methods overlook the intra-class structure within a category, especially in cases where there exists significant concept shift between the samples belonging to the same category. When samples with large concept shifts are forced to be pushed together, it may negatively affect the adaptation performance. Moreover, from the interpretability aspect, it is unreasonable to align visual features with significant differences, such as fighter jets and civil aircraft, into the same category. Unfortunately, due to such semantic ambiguity and annotation cost, categories are not always classified in detail, making it difficult for the model to perform precise adaptation. To address these issues, we propose a novel Memory-Assisted Sub-Prototype Mining (MemSPM) method that can learn the differences between samples belonging to the same category and mine sub-classes when there exists significant concept shift between them. By doing so, our model learns a more reasonable feature space that enhances the transferability and reflects the inherent differences among samples annotated as the same category. We evaluate the effectiveness of our MemSPM method over multiple scenarios, including UniDA, OSDA, and PDA. Our method achieves state-of-the-art performance on four benchmarks in most cases. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Benchmarking

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