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Demand Forecasting in Supply Chain Management using CNN-LSTM Hybrid Model 14

Demand Forecasting in Supply Chain Management using CNN-LSTM...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Naveen Parthasarathy, M.K. Rastogi, Shrey Alice, K. SRM Institute of Science and Technology Computer Science & Engineering Chennai India SRM Institute of Science and Technology Computing Technologies Chennai India

ISBN: (纸本)9798350335095

Efficient supply chain management is necessary to meet customer demands. Demand forecasting is a predictive analysis that estimates how much of a product or service a customer will need in the future. Accurate demand forecasting relies on past sales data and statistical estimates from test markets. Deep neural networks have gained popularity as a demand forecasting technique in supply chain management because of their high level of accuracy. The objective of this study is to use Long Short-Term Memory (LSTM) neural networks with hyperparameter tuning, as well as a hybrid model comprising Long Short-Term Memory and 1D Convolutional Neural Networks (CNNs), to predict the demand for eight medicinal drugs. The results obtained from the hybrid model will be compared with the LSTM models to select the best model for long-term forecasting. Demand forecasting has several applications, including inventory control, production planning, and market entry decisions © 2023 IEEE.

关键词： Long short-term memory

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Investigation on Mathematical Models and Emulators in Underwater Wireless Sensor Networks

Investigation on Mathematical Models and Emulators in Underw...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, C. Agnel Shyam Sukumaran, Rajeev Layolin Benisto, L.C. Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350313413

Underwater wireless communication is of paramount importance for various critical applications, including military surveillance, navigation, disaster prevention, and commercial activities. While terrestrial networks have made significant strides in protocol development and resource allocation, the unique underwater environment poses distinctive challenges that cannot be addressed by traditional wireless communication protocols. This paper conducts a comprehensive analysis of the hurdles associated with Underwater Wireless Sensor Network (UWSN) deployment, encompassing architectural considerations, protocol design, energy efficiency, communication channel allocation, and routing strategies. A crucial aspect of preparing for successful UWSN deployment is the creation of mathematical models that allow for the evaluation of performance metrics in the challenging underwater environment. Such models serve as a vital tool for predicting the behavior of communication systems and optimizing their performance before actual deployment. In this paper, we delve into the significance of constructing mathematical models tailored to the underwater environment, shedding light on how they can aid in the analysis of system performance. Furthermore, this paper provides a valuable resource by listing and discussing suitable mathematical approaches that can be applied to the unique underwater environment. These approaches are indispensable for the development of efficient underwater communication systems and applications. By addressing the challenges and emphasizing the importance of mathematical modeling, this research contributes to the advancement of underwater wireless communication technologies, facilitating their widespread and effective use in critical domains. © 2023 IEEE.

关键词： Acoustic Communication Applications Architecture Challenges of UWSN etc Underwater Sensor Network

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An intelligent Prediction of Customer Churn in Telecom Industry Using Business Analytics Classification Models 12

An intelligent Prediction of Customer Churn in Telecom Indus...

引用

12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Gohil, Shradhdha Ameria, Manas Vergin Raja Sarobin, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350318210

There are two common challenges faced by any internet service provider: the first being attracting new customers to grow their clientele, the second being retaining the existing customers and prevent them from leaving the company. It is established that client retention is six times cheaper than the cost of acquiring a new one. In order to achieve this, companies are increasingly investing their time and channeling their efforts to keep their clients on board. Understanding client behavior is key to identifying which of them have a high potential of leaving the company, thereby helping businesses develop appropriate retention strategies in response. This client attrition is commonly known as customer churn. Our proposed work focuses on churn prediction on Orange Company Client Dataset using four business data analytics models: Logistic Regression, KNN, Random Forest and Decision Tree. Based on the results, Random Forest boasts with the highest accuracy and F-1 score emerging as the clear winner. © 2023 IEEE.

关键词： Data Analytics

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Weberator: A low code backend generator tool 5

Weberator: A low code backend generator tool

引用

5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Koneti, Chandrakant Rechintala, Likhith Reddy Koneti, Jaya Sriharshita Indian Institute of Engineering Science and Technology Department of Information Technology Kolkata India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350305449

Nowadays, everyone desires to own a website, but creating it is tiresome. Developers can build a web application more effortlessly with the assistance of a software that automates the generation process. Several open-source softwares exist that help developers generate basic web application code. Some generation tools ease a developer's work by generating backend code. These generation tools work by retrieving information about the application and building it accordingly. One such generator tool is 'JHipster', an open-source software that generates a deployment-ready backend application. However, these generation tools are little challenging to operate and might be complex for many novice developers. Also, they require numerous prerequisite dependencies, and beginner developers might find it strenuous to comprehend and work with them. It is not everyone's cup of tea to apprehend the generation process and work with such high-level software. To bridge the gap between a beginner's technical knowledge and an advanced tool like JHipster, a mobile application named 'Weberator' that helps reduce the time and effort to create a backend application was developed. This paper shows the implementation of 'Weberator' that solves the issue of the technical gaps discussed above. This mobile application allows users to choose the technologies for their backend application and helps by generating the entire backend application into a ZIP format file. © 2023 IEEE.

关键词： Open source software

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Mood -Enhancing Music Recommendation System based on Audio Signals and Emotions 6

Mood -Enhancing Music Recommendation System based on Audio S...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Mounika, V. Charitha, Y. Dept.Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

The identity of emotional speech is a significant topic within the discipline of interactions between humans and computers. Many strategies of figuring out emotions in human speech had been introduced and installed through diverse researchers. To identify noises in audio documents is the purpose of one of these versions. Together with gender recognition and YouTube video will be played depending on mood, this suggested computer also features speech emotion detection, which listens for sentiments like happiness, rage, and sadness in audio cues. This output is sent as input to YouTube, which plays song within the user's mind, resulting in the person's temper to stabilize fast. Using the CNN characteristic extraction approach, the function sizes vector become processed with NumPy, and the audio class became carried out in MFCC. This research study mainly uses two programs: RAVDESS and SAVEE. Using the acquired datasets, a new version of the look was produced in-depth. The device area is the platform where the Google Colab is used to perform code execution. © 2023 IEEE.

关键词： Emotion Recognition

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Application Layer Denial of Service Attack Mitigation by Traffic Queuing 6

Application Layer Denial of Service Attack Mitigation by Tra...

引用

6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Maheswari, Harshit Saravanan, Renuka Devi Vellore Institute of Technology Chennai School of Computer Science and Engineering India

ISBN: (纸本)9798350394702

Denial of Service (DoS) attacks pose a significant threat to any business, organization or an individual trying to create their presence on the internet. At times DoS attacks can become very hard to identify if they are being caused by a malicious source or just due to some palpable reasons like resource overconsumption due to a large number of real users trying to access the service. While one can increase the server resources like the compute power and bandwidth in order to continue serving the content to the users but scaling might not be the most efficient thing to do in every scenario. Many modern methods currently available makes it an hassle for the genuine user to access the resources during an overload or attack. And generally resulting in flagging of legit users too during an attack, making the overall experience not ideal. Traditional methods preventing DoS attacks relied on using rule/signature-based techniques which have their own limitations and can fail to detect most sophisticated and low-rate attacks. This paper proposes a system that leverages modern Machine Learning algorithms to analyze user behavior which can be extracted from server access logs. A supervised learning model is implemented which uses various features which are extracted from the Access Log file of a server to detect malicious traffic at the Application Layer. The results show a promising accuracy, precision and recall. This system can be deployed as an additional layer of defence with other pre-existing solutions to improve the system's security. © 2023 IEEE.

关键词： Denial-of-service attack

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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AttentionFractalCovNet Architecture for Breast Ultrasound Image Segmentation 21

AttentionFractalCovNet Architecture for Breast Ultrasound Im...

引用

21st OITS International Conference on Information technology, OCIT 2023

作者： Agarwalla, Rhythm Ray, Satyajeet Kumar Paul, Saurav Sikkim Manipal Institute of Technology Computer Science and Engineering Majitar India

ISBN: (纸本)9798350358230

Tumor in the breast is a significant cause of unhealthiness among women. It is primarily the malignant-natured lesions that are a big reason for death via cancer among females. Segmentation of the tumor is complex due to the hazy boundaries and non-identical shapes and sizes. In this paper, an attention-based architecture, called AttentionFractalCovNet(AttFCN) architecture is proposed for the accurate and automatic segmentation of tumors. It takes inspiration from FractalCovNet architecture that forms a W-shape rather than the U-shape of the UNet architecture. Convolution blocks along with Attention Gates(AG) in the second decoding layer are used for achieving better segmentation results. Breast Ultrasound (BUS) images are used as input to segment out the lesions. Various matrices like Accuracy, MSE, Dice Coefficient(DC), Recall, Intersection over Union(IoU), Precision, and F1 Score(F1) were calculated for all the implemented architectures. It achieves higher DC and IoU values as compared to other architectures like SegNet, UNet, Re-sUNet, AttentionUNet(AU), and FractalCovNet(FracCN). Thus, the model is more suitable for the segmentation of tumors and can give better results than the existing architectures mentioned above. © 2023 IEEE.

关键词： Tumors

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Navigating Guest Sentiments: Comparative Analysis of Decision Tree and VADER for Hotel Review Analysis 2

Navigating Guest Sentiments: Comparative Analysis of Decisio...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Alex David, S. Begum, Almas Ruth Naveena, N. Hemalatha, D. Ravi Kumar, S. Vijayalakshmi, V. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Hindustan Institute of Technology & Science Department of Mathematics Chennai India

ISBN: (纸本)9798350368413

These days, while making hotel reservations, more thought is given to hotel reviews. The views of hotels have grown throughout the past few years. Because of the conflicting assessments, analysis is necessary to identify whether a hotel is positive or negative. The process of analyzing digital text to determine if the emotional tone of the communication is neutral, positive, or negative is known as sentiment analysis. Sentiment analysis is a new field that automates the study of such data by combining Machine Learning (ML) with Natural Language Processing (NLP). Initially, in this research work, dataset is constructed by crawling Tripadvisor data regarding hotel reviews using the hotel review dataset with two distinct algorithms, VADER and Decision Tree. This work has shown that, in comparison to Decision Tree algorithms, VADER is easier to build and determines the sentiment of the dataset. Hence, comparison shows that the Decision Tree method, VADER's accuracy on the hotel review dataset has slightly improved. © 2024 IEEE.

关键词： Natural language processing systems

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Enhancing Urban Mobility: An Adaptive Traffic Signal System for High-Density Cities 9

Enhancing Urban Mobility: An Adaptive Traffic Signal System ...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Shahriar, Rafi MD Ragib Rahman, Md Mostafizur Rahman, Mohyminur Mallick, Turjo Asim, Mudassir Salman Faisal, Tahsin Tabassum, Nuzhat Anannya, Tasmiah Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350319651

Urban regions in Bangladesh are increasingly plagued by traffic congestion, which increases travel times, lowers productivity, and increases emissions. Thus, the objective of this research is to develop an efficient adaptive traffic control system to reduce traffic congestion in high-density cities. To attain this objective, first, user requirements focusing to develop a digital solution were elicited through surveys. Then, a conceptual framework was proposed to address the revealed requirements followed by developing the adaptive system. The proposed system offers vehicle detection through YOLOv7 algorithm, vehicle prioritization on the road, dynamic traffic signal duration, overriding as well as assuring instant emergency access. Finally, the system was evaluated by measuring the performance of YOLOv7 model for predicting vehicles and by assessing the exe-cution of system functionalities. The evaluation results revealed a commendable level of prediction accuracy, ranging from 93% to 100%. Furthermore, the system functionalities were successfully executed with an average success rate of 92 % and an average execution time of 2.0 ± 0.31 seconds. © 2023 IEEE.

关键词： Traffic congestion

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