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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Gelam, Sindhu Sahithi Manikanta Thota, Sai Naga Voleti, Sai Anushree Nair, Lekshmi S. Teja Tummala, Durga Manikanta Naga Surya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Clappana India

ISBN: (纸本)9783031699856

Crowdsourcing platforms have emerged as a powerful tool for harnessing the collective intelligence and expertise of a diverse pool of workers. However, efficiently allocating tasks to the most suitable workers remains a significant challenge due to the heterogeneity of workers, the complexity of tasks, and the dynamic nature of the crowdsourcing environment. This study addresses the task allocation problem on crowdsourcing platforms by proposing a novel mentorship mapping approach. The proposed approach categorizes workers into experienced and inexperienced workers based on their experience, successful completion percentage, and approved on-time rate. Mentorship mapping leverages the concept of mentorship, where experienced workers guide and assist less experienced workers, to enhance task completion rates and overall productivity. The proposed approach involves identifying and pairing experienced workers with less experienced workers based on their skill sets, experience levels, and task requirements. This mentorship-driven task allocation strategy aims to optimize worker-task matching, leading to efficient workforce utilization and improved task completion outcomes. The benefits of mentorship mapping include faster task completion times, reduced errors, and enhanced worker satisfaction, contributing to a more efficient and productive crowdsourcing ecosystem. © IFIP International Federation for Information Processing 2024.

关键词： Mapping

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Securing Images: Cryptographic Approach in Big Data Scenario 15

Securing Images: Cryptographic Approach in Big Data Scenario

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

作者： Shah, Kriti Priya Sah, Subodh Narayan Kothari, Osho Vishwas, H.N. Amrita Vishwa Vidyapeetham Amrita School of Computing Department. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350370249

Digital Image as a form of data is a critical component of the digital age. Therefore, Image Encryption and Decryption is one of the important aspect for modern communication. Image security for such digital images remain one of the important questions to be answered [14][15]. Among all the advanced Encryption and Decryption algorithm, Advanced Encryption Standard (AES) and Chaotic Map Algorithm remains the prominent approaches for image security through advanced encryption and decryption approaches. This Study undertakes these two algorithm for encryption and decryption approaches for image. AES on one hand is considered for its strong security and consistent performance whereas Chaotic Map is considered for its dynamics for encryption. In a Big Data scenario, performance efficiency, scalability, and robustness of these algorithms are critical factors due to the large volumes of image data involved. Further statistical analysis including Entropy, Mean Squared Error (MSE), Correlation Coefficient, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) are considered for both the algorithms. Additionally both the algorithms are further evaluated on the basis of encryption and decryption time and key sensitivity for the security reasons. © 2024 IEEE.

关键词： AES (Advanced Encryption Standard) Big Data Chaotic map-based encryption Image decryption Image encryption Image Security Statistical Analysis

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Scheduling in Time-Sensitive Networks Using Deep Reinforcement Learning 1

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International Conference on Applied Soft computing and Communication Networks, ACN 2023

作者： Gopalakrishnan, Abhilash Duttagupta, Subhasri Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Kollam India

ISBN: (数字)9789819720040

ISBN: (纸本)9789819720033

Ethernet and wireless-based technologies play an important role in the Internet of Medical Things. The new healthcare scenarios require a deterministic communication system with real-time capabilities. In remote surgery, for example, the video latency is limited to 200 ms. In addition to latency, packet loss above a certain threshold can have a serious impact on the surgery. The majority of IP traffic today is served as best effort. Although assigning priority classes to flows can help, it is insufficient. A typical solution is to use high-speed media layers such as EtherCAT. However, these approaches do not integrate well with Ethernet. In this context, IEEE Time-Sensitive Networking (TSN) standards are gaining traction as a key enabler of real-time capabilities. TSN enables real-time communication by providing mechanisms for controlling latency, jitter, and packet loss. The purpose of this paper is to identify a scheduling strategy to minimize the violation of an end-to-end delay requirement while working with mixed-priority traffic, including time-critical ones. The majority of related work focus on static scheduling and cannot be learned continuously. This paper employs a deep reinforcement learning technique to enable dynamics and incremental learning. The scheduling problem is modeled as a Markov decision process, in which constraints are mapped to Rewards. In the proposed solution, learning converges in a few epochs and the results provide a feasible schedule. The schedule is then evaluated using simulation, which demonstrates that the deterministic requirements are met. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews

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Soft computing 2024年第13-14期28卷 7627-7647页

作者： Kothuri, Sri Raman RajaLakshmi, N.R. Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India

Sentiment analysis of online car reviews is a significant process in natural language processing. Numerous car enthusiasts and influencers create video reviews of automobiles on platforms for example YouTube. In realistic settings for sentiment analysis within this context, the task includes evaluating spoken reviews, the video content, and the emotions expressed by both the content creators and the viewers as reflected in the comments section. The vulnerability to facing circumstances in real-world scenarios where critical components are necessary for performing sentiment analysis on multimedia car reviews may unexpectedly be absent. To overcome this issue, siamese capsule gorilla troops network-based multimodal sentiment analysis for car reviews is proposed in this research work. Initially, pre-processing and feature extraction are performed on the audio, video and text information. The feature fusion is performed by enhancing spiking neural networks with geometric attention fusion network. The fused feature is fed as an input to siamese capsule gorilla troops network which classifies the emotion states as valence, arousal, and trustworthiness. Also, the algorithm called gorilla troop optimization (GTO) optimizes the classification error of the proposed siamese capsule network. Finally, this research work assesses high- and low-level features across modalities, introduces a novel enhancing spiking neural networks with geometric attention fusion network, enhances classification accuracy through new audio, video, and text embeddings, and boosts system robustness by integrating incomplete multimodal data. The achieved accuracy, precision, recall, and F-score values are 99%, 98.55%, 98.75%, and 98.02%, respectively, for the active multimodal sentimental analysis in car review (MuSe-CaR) dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Sentiment analysis

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IoT-Based Solution for Monitoring Gas Emission in Sewage Treatment Plant to Prevent Human Health Hazards 4th

IoT-Based Solution for Monitoring Gas Emission in Sewage Tre...

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4th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2023

作者： Ullas, S. Maheswari, B. Uma Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9789819965854

As the global population grows and water resources become scarce, the only way to effectively conserve and utilise them is by adequately treating and reusing them. In Sewage Treatment Plants, ensuring a safe gaseous environment for the worker’s health is essential. Exposure to toxic gases for a long duration may cause health issues for workers. It is essential to understand these factors and take necessary precautions to prevent further deterioration of their health. In this work, we aim to develop a sensor-based system that measures the intensity levels of toxic gases like Methane, Ammonia, Carbon dioxide, and Hydrogen Sulphide emitted from the plant and spread to its environment and to alert at the right time to safeguard the humans around. We compared these gas levels with data captured in good air to demonstrate the high density of the toxic gases in the plant. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ammonia

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Revolutionizing Healthcare: Cloud-Based Health Information Exchange and Disease Prediction

Revolutionizing Healthcare: Cloud-Based Health Information E...

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2024 International Symposium on computing and Intelligent Systems, SCI 2024

作者： Nayini, Sai Nithin Gade, Vishwas Gudapareddy, Mani Prakash Reddy Peddineni, Varshith Beena, B.M. Department of Computer Science &Engineering Amrita School of Computing AmritaVishwa Vidyapeetham Karnataka Bangalore560035 India

A cloud-based system called Health Information Exchange (HIE) is. Hosted on Amazon Web Services (AWS). This system provides logins, for doctors, administrators and patients. The primary objective of this work is to revolutionize healthcare data sharing by providing a convenient platform. Patients are empowered to manage their health by selecting their symptoms and receiving disease predictions. AWS is utilized to orchestrate the cloud-based infrastructure, which includes SageMaker for constructing machine learning models RDS for a PostgreSQL database, EC2 for hosting the Django server, Lambda for deploying machine learning model APIs CloudWatch for resource monitoring and IAM, for access control. The core element of this system is the implementation of a classification framework that optimizes the organization and retrieval of data. This innovative approach enhances data accessibility while streamlining healthcare processes to facilitate decision making and patient care. By integrating AWS services, it also creates a scalable foundation that supports the growth of a dynamic retail environment. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Machine learning

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A Hybrid Ensemble Model for Drug Recommendation with Sentiment Analysis from Textual Reviews 15

A Hybrid Ensemble Model for Drug Recommendation with Sentime...

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

作者： Kumar, Nirmal K. Yesudas, Gokul Yoonus, Muhammed Azeem Faheem, V. Rani, Siji S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Kollam Kerala India

ISBN: (纸本)9798350370249

The usage of online recommender systems in healthcare, pharmaceuticals, and hospitals is growing. Nowadays, the vast majority of consumers research potential prescriptions online before consulting their doctors for a variety of medical issues. Modern drug recommendation systems frequently pay no attention to the unique needs of a patient and don’t wait for the expectations of the patient. To this, we propose a novel hybrid ensemble model that incorporates Deep Learning, Machine Learning, and Sentiment Analysis. The contribution of the TensorFlow/Keras neural network is in drug review analysis for sentiment, meanwhile LightGBM classifier that offers an alternative approach is another part of the network. Besides TextBlob and a dictionary-based annotation approach, machine learning taking place during classification brings value to sentiment analysis. Forecasts of authorities are pooled, weighted by review influence, and clashed to produce drug-specific studies. This multi-faceted strategy is better compartmentalized and tailor to each patient’s needs, taking emotional state into account, thereby patients are able to access more accurate drug advice and healthcare professionals to make informed decision. Qualitative analysis of the efficiency the model will be carried out using the confusion matrices. Thus, by means of the research on hand, we expect the transformation of the drug recommendation system taking into consideration and patient-individualized system, which will contribute to the improvement of healthcare delivery. ©2024 IEEE.

关键词： Contrastive Learning

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Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

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Enhancing Drug Recommendations: A Modified LSTM Approach in Intelligent Deep Learning Systems 5

Enhancing Drug Recommendations: A Modified LSTM Approach in ...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Siji Rani, S. Shilpa, P. Menon, Aswin G. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri690525 India

The overwhelming burden on the healthcare system has resulted in increased mortality rates as individuals struggle to receive appropriate medical attention. Machine learning has demonstrated its efficacy across diverse applications, fueling a growing trend in the realm of innovative automation. This is particularly facilitated by drug recommendation systems, which possess the capability to significantly reduce the dependence on specialized assistance. Faced with limited access to medical expertise, a considerable number of individuals have turned to self-medication, often worsening their health conditions. In response to this critical scenario, this paper introduces an inventive drug recommendation system which helps to lessen the strain healthcare professionals. Here, we present the development of a medication recommendation system based on LSTM, utilizing user-provided symptoms and the model's evaluations of past drug recommendations. The results of the comparison between Random Forest, SVM, and LSTM indicated that the modified LSTM-based recommendation system outperforms the other two models, achieving a 94% accuracy rate. © 2024 The Authors. Published by Elsevier B.V.

关键词： Support vector machines

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Enhancing Urban Waste Management Through Smart Dustbins and IoT-Based Technology 3rd

Enhancing Urban Waste Management Through Smart Dustbins and ...

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3rd International Conference on Advanced computing and Intelligent Technologies, ICACIT 2023

作者： Rajeshvaran, C.R. Rohit, R. Vignesh, S.E. Lakshika, K. Nair, Prashant R. Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9789819719600

A smart waste management system is implemented with two models of smart dustbins and a mobile application. The dustbins have features like AI segregation, automatic open-close mechanism, level display, and odor detection. The mobile app enables communication between workers, officials, and the public for efficient waste collection. The system utilizes IoT technology for automation and sustainability. It promotes recycling, offers real-time tracking, and enhances worker coordination. The smart fleet management system ensures efficient waste collection, with real-time updates for workers and the ability to report issues. This integrated system provides a sustainable, efficient, and technology-driven approach to urban waste management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Waste management

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