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Finite Automata Application in Monitoring the Digital Scoreboard of a Cricket Game 1

1st International Conference on Computational sciences and Sustainable Technologies, ICCSST 2023

作者： Kaushik, K. Pavithra, L.K. Subbulakshmi, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Cricket is one of the most viewed sports in the world and tracking of this game is very important throughout the gameplay. The application of automata in sports is least explored and this paper proposes the use of automata theory in cricket. The paper sheds light upon the potential application of finite automata concept in cricket which proposes proper tracking flow of the game. The major focus of the paper is shown upon ball-tracking and on-strike batsman tracking with each tracking system being explained in detail using the state diagrams. With the help of Deterministic Finite Automata (DFA), it is possible to achieve the flow of track and thus, helps in efficient tracking of the sport. It also minimizes the rate of committing the tracking errors during the gameplay. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sports

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Bridging the Gap Between Online and In-Store Shopping: Fashion Recommendations and Virtual Try-On 5

Bridging the Gap Between Online and In-Store Shopping: Fashi...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Vaishnavi, V.V. Narendra, G.O. Boovaraghavan, Aravinda Anbarasi, L. Jani Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

The development of extensive clothing databases has significantly advanced the field of clothing recognition and recommendation systems. However, existing datasets often suffer from a limited number of annotations and face challenges when applied to diverse, real-world scenarios. To address these limitations, the proposed research work leverages DeepFashion, a comprehensive large-scale garment dataset renowned for its detailed annotations. The primary objective is to develop an advanced fashion recommendation system combined with a virtual try-on feature using cutting-edge deep learning techniques. This system is designed to enhance the online shopping experience by providing personalized clothing recommendations and enabling users to visualize how the recommended garments would appear on them. By inputting an image, users can receive recommendations of similar clothing items by using powerful feature extraction capabilities of the ResNet50 model and the efficient retrieval mechanism of the Nearest Neighbor algorithm. Following the recommendation phase, the system employs the U2Net model for image segmentation to facilitate the virtual try-on feature. U2Net's two-level layered U-structure architecture performs well in detecting salient objects, producing high-resolution segmented images that isolate the clothing item from the background and the human body in the original input. This segmentation is crucial for accurately overlaying the recommended garment onto the user's image, creating a realistic virtual try-on experience. Following the recommendation phase, the system employs the U2Net model for image segmentation to facilitate the virtual try-on feature. The proposed research aims to address the problems of current fashion recommendation systems by utilizing the extensive and richly annotated DeepFashion dataset and integrating advanced deep learning models. © 2024 IEEE.

关键词： Recommender systems

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Enhancing Intrusion Detection systems for 5G Networks using AI 1

Enhancing Intrusion Detection Systems for 5G Networks using ...

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1st International Conference on Software, systems and information technology, SSITCON 2024

作者： Gurushanker, Abirami Anandakumaran, Kirtanaa Christopher Columbus, C. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

As 5G networks advance, cybersecurity vulnerabilities in IoT devices are increasingly exposed. While intrusion detection systems (IDS) have progressed, there is a notable gap in IDS specifically designed for the unique protocols and complexities of 5G networks. Current research often emphasises binary classification, missing the nuanced multiclass classification needed for effective threat detection. This study addresses these deficiencies by integrating machine learning and deep learning techniques within the 5G core architecture to develop an advanced IDS. Utilising TCP/IP flow statistics and PFCP signalling attack data, and focusing on control plane signalling between the Session Management Function (SMF) and User Plane Function (UPF), various models are evaluated for detecting anomalous activities. The study achieves 97% accuracy with TCP/IP data and 69% with PFCP data. The proposed IDS, tailored for 5G-specific workloads and trained on PFCP protocol data, enhances detection capabilities and addresses significant security challenges, improving the resilience of IoT devices in 5G networks. © 2024 IEEE.

关键词： Network intrusion

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Exploring the Efficacy of Federated-Continual Learning Nodes with Attention-Based Classifier for Robust Web Phishing Detection: An Empirical Investigation

Exploring the Efficacy of Federated-Continual Learning Nodes...

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2024 International Conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Jesher Joshua, M. Adhithya, R. Sree Dananjay, S. Revathi, M. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350363289

Web phishing poses a dynamic threat, requiring detection systems to quickly adapt to the latest tactics. Traditional approaches of accumulating data and periodically retraining models are outpaced. We propose a novel paradigm combining federated learning and continual learning, enabling distributed nodes to continually update models on streams of new phishing data, without accumulating data. These locally adapted models are then aggregated at a central server via federated learning. To enhance detection, we introduce a custom attention-based classifier model with residual connections, tailored for web phishing, leveraging attention mechanisms to capture intricate phishing patterns. We evaluate our hybrid learning paradigm across continual learning strategies (cumulative, replay, MIR, LwF) and model architectures through an empirical investigation. Our main contributions are: (1) a new hybrid federated-continual learning paradigm for robust web phishing detection, and (2) a novel attention + residual connections based model explicitly designed for this task, attaining 0.93 accuracy, 0.90 precision, 0.96 recall and 0.93 f1-score with the LwF strategy, outperforming traditional approaches in detecting emerging phishing threats while retaining past knowledge. © 2024 IEEE.

关键词： Phishing

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Gait Image Classification Using Deep Learning Models for Medical Diagnosis

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computers, Materials & Continua 2023年第3期74卷 6039-6063页

作者： Pavitra Vasudevan R.Faerie Mattins S.Srivarshan Ashvath Narayanan Gayatri Wadhwani R.Parvathi R.Maheswari School of Computer Science and Engineering Vellore Institute of TechnologyChennaiTamil NaduIndia centre for smart grid technologies scopevellore institute of technologychennaitamil naduindia

Gait refers to a person’s particular movements and stance while moving *** each person’s gait is unique and made up of a variety of tiny limb orientations and body positions,they all have common characteristics that help to define *** identifying such characteristics that are difficult to spot by the naked eye,can help in monitoring the elderly who require constant care and *** silhouettes is the easiest way to assess and make any necessary adjustments for a smooth *** also becomes an important aspect of decision-making while analyzing and monitoring the progress of a patient during medical *** images made publicly available by the Chinese Academy of sciences(CASIA)Gait Database was used in this *** evaluating using the CASIA B and C datasets,this paper proposes a Convolutional Neural Network(CNN)and a CNN Long Short-TermMemory Network(CNN-LSTM)model for classifying the gait silhouette *** learningmodels such as MobileNetV2,InceptionV3,Visual Geometry Group(VGG)networks such as VGG16 and VGG19,Residual Networks(ResNet)like the ResNet9 and ResNet50,were used to compare the efficacy of the proposed *** proved to be the best by achieving the highest accuracy of 94.29%.This was followed by ResNet9 and CNN-LSTM,which arrived at 93.30%and 87.25%accuracy,respectively.

关键词： CNN CNN-LSTM transfer learning CASIA datasets gait analysis

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Analysis of community question-answering issues via machine learning and deep learning:State-of-the-art review

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CAAI Transactions on Intelligence technology 2023年第1期8卷 95-117页

作者： Pradeep Kumar Roy Sunil Saumya Jyoti Prakash Singh Snehasish Banerjee Adnan Gutub Department of Computer Science and Engineering Indian Institute of Information TechnologySuratIndia Indian Institute of Information Technology DharwadIndia Department of Computer Science and Engineering National Institute of TechnologyPatnaIndia The York Management School University of YorkYorkUK Computer Engineering Department College of Computer&Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia

Over the last couple of decades,community question-answering sites(CQAs)have been a topic of much academic *** have often leveraged traditional machine learning(ML)and deep learning(DL)to explore the ever-growing volume of content that CQAs *** clarify the current state of the CQA literature that has used ML and DL,this paper reports a systematic literature *** goal is to summarise and synthesise the major themes of CQA research related to(i)questions,(ii)answers and(iii)*** final review included 133 *** research themes include question quality,answer quality,and expert *** terms of dataset,some of the most widely studied platforms include Yahoo!Answers,Stack Exchange and Stack *** scope of most articles was confined to just one platform with few cross-platform *** with ML outnumber those with ***,the use of DL in CQA research is on an upward trajectory.A number of research directions are proposed.

关键词： answer quality community question answering deep learning expert user machine learning question quality

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Detecting potholes with a machine learning algorithm for road safety 3

Detecting potholes with a machine learning algorithm for roa...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Singh, Yuvraj Roy, Sritama Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798350364569

An important part of a country's infrastructure are its roads, which provide a vast transportation network necessary for the movement of people and goods. Road transport is the most cost-effective means of transport in India because it is widely available in densely populated areas. On the other hand, potholes continue to be a major problem that cause delays in transit and, regrettably, fatalities. A system and corresponding algorithms have been developed to detect the probability of potholes in order to tackle this problem. With the least amount of work, Single Shot Detection (SSD) technology is essential to accomplishing the goal. SSD's fast object identification and localisation in photos, along with its real-time processing capabilities, make it possible to detect potholes on road surfaces with efficiency. By utilising SSD technology, the proposed system offers a dependable means of reducing the detrimental effects of potholes on traffic safety and the efficiency of transportation. © 2024 IEEE.

关键词： Motor transportation

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Optimising Makespan Minimization and Performance at the Same Time with a Dynamic Cloud Task Scheduling Comparative Study of MOSOS and PSO 5

Optimising Makespan Minimization and Performance at the Same...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Jain, Anegha Nath, Megha Rama Parvathy, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The practice of allocating available resources, such as personnel, money, time, or materials, to different tasks, projects, or activities is known as resource allocation. Effective resource allocation is crucial for optimizing productivity, achieving goals, and maximizing efficiency within an organization or project. Resource allocation in cloud computing can face several challenges. These include over-allocation, where resources are provisioned excessively, leading to increased cost, and under-allocation, which can result in performance bottlenecks. Cloud environments often have to support a variety of different workloads, each with its own resource requirements. This can make it difficult to allocate resources efficiently whilst maintaining the Quality of Service (QoS). Tackling such issues, our paper demonstrates the workings of the MOSOS (multi-objective symbiotic organism search) algorithm to optimize the performance and cut down on makespan and cost. We simulate MOSOS alongside the Particle Swarm Optimization Algorithm (PSO) to understand whether PSO outperforms MOSOS or whether MOSOS accomplishes similar results to PSO. The results back up MOSOS in terms of makespan but fail when compared against PSO's execution times. We find that PSO uses a lesser number of parameters and makes fewer function calls as opposed to MOSOS. Thus, we can argue that in dynamic cloud environments where time is essential, PSO may perform better as compared to MOSOS. © 2024 IEEE.

关键词： Cloud computing Evolutionary algorithms makespan optimization resource scheduling

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Adopting Ensemble Learning and Machine Learning Techniques for Predictive Modeling in Traffic Data Analysis 3

Adopting Ensemble Learning and Machine Learning Techniques f...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Santhosh Narayanan, B. Shyamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375190

In this study, the trends of traffic data are analyzed using predictive analysis techniques applied to the NYC taxi dataset. Various models were tested for forecasting travel time, fare, and speed. Notably, the Decision Tree Regression model demonstrated outstanding performance with an impressive R2 score of 0.9995 for travel time prediction. Additionally, an ensemble model consisting of Linear Regression and Decision Tree Regression achieved an accuracy of 0.93 for fare prediction. Furthermore, another ensemble model combining LSTM and DBN attained an R2 score of 0.90 for speed prediction. This study highlights the effectiveness of diverse predictive modeling techniques in accurately forecasting these features. © 2024 IEEE.

关键词： Long short-term memory

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Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Emotional Well-Being 2

Smart IoT-Based Emotion Sensing Wearable: Revolutionizing Em...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Kannapiran, Pritika Roy, Sritama School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai600127 India

ISBN: (纸本)9798350366570

In the fast-paced world of today, mental health has become a critical issue, with emotional stability being a key component of general health. Traditional methods of detecting and managing emotions often fall short due to their reliance on verbal and facial cues, leaving individuals with conditions such as Alexithymia or Autism Spectrum Disorder (ASD) at a disadvantage. This paper introduces a novel solution: the Smart IoT-Based Emotion Sensing Wearable, is designed to revolutionize emotional sensing and management. The primary goal of the research is to create a wearable gadget that can track vital signs and instantly identify different emotions. Using the Internet of Things (IoT), machine learning, and cloud computing, this wearable device bridges the gap for people with special needs and improves emotional understanding for all users by providing real-time emotion recognition and targeted help. © 2024 IEEE.

关键词： Emotion Recognition

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