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Digital Transformation in B2B Destination Management Companies: An Intuitive Django Web application 9

Digital Transformation in B2B Destination Management Compani...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Khandelwal, Rashi Thakur, Pallavi Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350394474

This research paper presents a comprehensive case study on the digital transformation of the back-office operations within destination management companies. In an era marked by technological advancements, DMCs find themselves at the intersection of manual, labor-intensive processes and the vast potential of modern digital solutions. The study examines the pivotal role played by an innovative web application, developed and deployed by the author, in automating and streamlining the back-office functions of a DMC. This transformative project replaces manual collaboration on Excel spreadsheets, voucher generation, and invoice processing with a secure, efficient, and user-friendly system. The technology stack utilized includes HTML, CSS, Bootstrap, JavaScript, jQuery for the front end, Python and Jinja for the backend, MySQL for database management, and the Django framework, hosted on Amazon Web Services (AWS). Through this case study, we delve into the tangible impacts of this digital transformation, including enhanced efficiency, reduced operational overhead, and improved responsiveness to partners and clients. The system offers real-time access to services, inventory, and pricing, simplifying the booking process, and enabling data-driven decision-making through advanced analytics and reporting. Additionally, the paper explores the broader implications of this project for the B2B destination management industry as a whole. It highlights the potential for technology to revolutionize traditional workflows, improve customer experiences, and increase competitiveness in a global market. In a nutshell, this case study of digital transformation serves as a testament to the power of technology in reshaping traditional B2B destination management operations. This research sheds light on the transformative benefits of automation, data integration, and real-time accessibility, emphasizing the potential for similar innovations to drive the industry toward greater efficiency and cli

关键词： Efficiency

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Design and Implementation of External Storage Large-Scale Graph Computing System 23

Design and Implementation of External Storage Large-Scale Gr...

引用

7th International Conference on High Performance Compilation, Computing and Communications, HP3C 2023

作者： Liu, Lingbin Huang, Jianqiang Huang, Dongqiang Bian, Haodong Wang, Xiaoying Department of Computer Technology and Application Qinghai University China

ISBN: (纸本)9781450399883

With the rise of big data, graph computing has become prevalent in many fields. To effectively address such problems, large-scale graph computing systems have emerged. Most existing systems adopt memory-based computing frameworks. However, the rapid growth of data scale often leads to insufficient memory storage. To tackle these issues, an external storage-based graph computing system called DCGraph has been designed and implemented. The bottleneck of large-scale graph computing systems based on single-machine external storage usually lies in external storage I/O. Hence, DCGraph has been optimized for external storage I/O. In the preprocessing stage, graph data is compressed and transformed into a two-dimensional CSC format. Each block records the offset of its data. This guarantees that data of the same target vertex is continuously stored in external storage within the same block. DCSC format is selected for compressing data blocks with many vertices of zero in-degree. Additionally, a jump-out calculation mode suitable for the CSC format is designed for specific graph calculation algorithms. During data reading, it is determined whether to skip the current record based on its subscript and offset. Selective scheduling is used to skip inactive data blocks during each layer iteration to reduce unnecessary data reading and processing. This approach reduces both the number and total amount of I/Os. Experimental results show that compared to GridGraph, DCGraph achieves a speedup ratio of 1.5 to 2.8 times while reducing the total amount of I/Os in calculation to less than half of that of GridGraph. CCS CONCEPTS © 2023 ACM.

关键词： Big data

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A Comparative Study on Reinforcement Learning in Disease Prediction on Medical Data

A Comparative Study on Reinforcement Learning in Disease Pre...

引用

2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems, ADICS 2024

作者： Nijana, V. Rajendran, P. Selvi Hindustan Institute Of Technology And Science Computer Application Department India Hindustan Institute Of Technology And Science Department Of Computer Science And Engineering India

ISBN: (纸本)9798350364828

Medical imaging has been used extensively in healthcare in recent years for a variety of purposes, including disease diagnosis, treatment planning, and tracking the course of an illness. These applications entail taking pictures of the afflicted organ using a variety of modalities. Image segmentation and classification is two important process performed in disease diagnostic application. Deep reinforcement learning approach applied in many gaming application. DRL provides the accurate result in many real world applications, so researchers pay attention in DRL in medical data that helps the physicians in treating the patient. This study focus on surveying the various applications such as Image registration, lesion localization, image segmentation, image classification and Landmark detection using deep reinforcement learning in healthcare. In addition, this study investigates on the various DRL algorithms (Qlearning, deep deterministic policy gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TDDDPG), Deep Q Network (DQN) and Soft Actor-Critic (SAC) in Alzheimer's disease prediction. Among the DRL methods the DDPG achieved the highest accuracy in detecting the Alzheimer's disease with 97%. © 2024 IEEE.

关键词： Forecasting

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Transforming Software Requirements into User Stories with GPT-3.5 -: An AI-Powered Approach 2

Transforming Software Requirements into User Stories with GP...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Oswal, Jay U. Kanakia, Harshil T. Suktel, Devvrat Sardar Patel Institute of Technology Master of Computer Application Department Mumbai India

ISBN: (纸本)9798350327533

In today's dynamic software development landscape, Agile methodologies have established themselves as essential for organizations striving to swiftly adapt to evolving customer needs and market demands. A cornerstone of the Agile framework is the concept of User Stories, a concise format for expressing software requirements from an end-user perspective. The manual generation of User Stories from unstructured requirement texts proves to be a labor-intensive endeavor, riddled with challenges related to maintaining consistency and adhering to specific organizational practices. This research underscores the profound importance of Agile Methodology in contemporary software development and underscores the critical role that User Stories play within this framework. To address the inherent inefficiencies associated with manual User Story creation, this paper introduces a novel and innovative AI-powered approach that uses the advanced capabilities of the GPT-3.5 language model. This approach facilitates a seamless and efficient transformation of software requirement text into standardized User Stories by studying various prompting techniques. In this research paper the practical implementation of our approach have been illustrated, we have developed an application harnessing the natural language processing capabilities of GPT-3.5 where in the user can enter or upload the requirement text and it will be transformed into user stories. © 2024 IEEE.

关键词： Agile Data Mining Few Shot Prompting Generative Pre-Trained Transformers Large Language Model Natural Language Processing Prompt Engineering Software Requirements User Story

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Social Media Content Recommendation System using Knn Algorithm 3

Social Media Content Recommendation System using Knn Algorit...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Hariharan, R. Janit, R. Sophia Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9798350399264

Online streaming platforms for YouTuber short films are now widely used. Sites like YouTube Shorts provide an excellent platform for content creators to share their knowledge, ideas, and interesting information with their audience. There are no limits to creativity in the world of short videos. Billions of people create, edit, watch and share short videos on YouTube Shorts. Whether you want to share your voice with the world or create fun videos for your friends, all you need is your phone and your ideas. Machine learning algorithms are used to analyze large amounts of user behavior data such as: B. Search, watch videos, like, comment, subscribe. By analyzing this data, recommendation systems can predict user preferences and recommend videos that users are likely to like. Companies are using social media to grow their business, and data mining and analytics are very important today. This article describes trend analysis for short videos on YouTube. Creators are encouraged to create compelling video content. These user characteristics such as views, comments, likes and dislikes are used for analytics. Analytics can use this algorithm as a supervised algorithm to classify other machine learning models and filter based on content. © 2023 IEEE.

关键词： Python

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Enhancing Scalability in Sharding Blockchain via Interoperability Protocol 5th

Enhancing Scalability in Sharding Blockchain via Interoperab...

引用

5th International Conference on Trends in Computational and Cognitive Engineering, TCCE 2023

作者： Maurya, Alka Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology Gorakhpur India

ISBN: (纸本)9789819719228

Blockchain technology has shown immense promise in revolutionizing various industries by offering secure and open decentralized solutions. Popular blockchain networks still struggle with scalability, which limits their potential for widespread adoption. A suggested alternative, sharding, divides the blockchain into more manageable, smaller chunks to boost throughput and transaction capacity. Sharding is the extensively used approach to the trilemma of simultaneously attaining decentralization, security, and scalability in traditional blockchain systems. Although sharding has many advantages, it also poses problems with inter-shard communication and data consistency. The focus of this thesis is to explore how interoperability between shards can be leveraged to enhance scalability in sharding blockchains. Our goal is to increase the blockchain ecosystem's scalability, efficiency, maintaining transparency, security, throughput, and usability by enabling seamless communication and data sharing between shards. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

引用

2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Comparison of Transfer Learning algorithm in predicting Alzheimer's disease 1

Comparison of Transfer Learning algorithm in predicting Alzh...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Yashodhar, Archana Kini, Shashidhar Department of Computer Science and Application Mangaluru India Srinivas Institute of Technology Master of Computer Application Mangaluru India

ISBN: (纸本)9798350348057

Many neurogenerative diseases affect today's generation. The causes of this condition are unclear. Alzheimer's disease is one of that diseases, and elderly peoples suffer a lot from this. In this article, we applied several deep learning transfer learning approaches to categorize distinct phases of Alzheimer's disease. Different measures are used to assess each strategy, which leads to better results. © 2023 IEEE.

关键词： Deep learning

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Exploring Deep Learning Techniques for Sentiment Analysis on IMDb Movie Reviews Dataset

Exploring Deep Learning Techniques for Sentiment Analysis on...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Sharma, Madhav Saini, Hukum Chand Sikarwal, Pushpendra Kumar Jaipur India Department of Engineering and Technology Jaipur India Department of Computer Application Jaipur India

ISBN: (纸本)9798350378047

Deep learning methods have changed the way we deal with natural language processing (NLP), and now our models can generate Textual Data almost as well like humans, but it was recent advances that introduced these revolutionized mechanisms for understanding complex human languages. The task of sentiment analysis-identifying the sentiment in text like movie reviews- is a fundamental one for NLP. This paper focuses on utilizing deep learning techniques, more specifically recurrent neural networkswith Long Short-Term Memory (LSTM) cells for sentiment analysis in IMDb movie reviews dataset. The model proposed in this study uses word embeddings [1], LSTM layers which are integrated as the temporality and gives a way to understand temporal dependencies important for analyzing meaning representations of sequential text data for sentiment classification. We start by pre-processing the IMDb data - cleaning and tokenizing text-data, after which we convert words into dense vector representations using pre-trained word embeddings. These embeddings are then processed by the LSTM layers, which helps to understand the context and sequential information [2]. © 2024 IEEE.

关键词： Spatio-temporal data

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Zero-Shot Object Detection Using YOLO

Zero-Shot Object Detection Using YOLO

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Anushiya, R. France, K. Hindustan Institute of Technology and Science Department of Computer Application Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

Zero-shot object detection is an emerging approach that aims to detect unseen object categories by leveraging the knowledge from known categories. In this study, we propose a novel zero-shot object detection framework based on the popular YOLO (You Only Look Once) architecture. Our approach addresses the challenge of detecting objects that have not been seen during training by learning a mapping between the visual features of unseen categories and their textual descriptions. We introduce a semantic embedding space that captures the relationship between visual and textual representations and use it to generate visual features for the unlabeled object instances. By aligning these visual features with the textual descriptions of unseen categories, we enable the detection of previously unseen object classes. Our experimental results demonstrate the effectiveness of our approach, achieving competitive performance for both unseen and seen object categories on kaggle datasets. Moreover, our framework's incremental learning capability allows for the continuous incorporation of new unseen object categories without the need for retraining the entire system. The proposed zero-shot object detection method opens up new opportunities for various applications, such as autonomous vehicles, surveillance systems, and robotics, where the ability to recognize novel objects is crucial with the high level of Accuracy-98.2%, Precision-97.9%, Recall-97.8%, F1 score-96.8% using You Only Look Once (YOLO) in machine learning algorithm. © 2024 IEEE.

关键词： Zero-shot learning

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