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Multi-Modal Deep Learning for User-Centric Car Recommendations

Multi-Modal Deep Learning for User-Centric Car Recommendatio...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： M. Saravana Karthikeyan A. Alice Blessie J. Relin Francis Raj R. Santhana Krishnan K. Haribabu S. Balamurugan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Government College of Engineering Tirunelveli India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

This paper presents a Multi-Modal Deep Learning framework for User-Centric Car Recommendations, aimed at transforming the car selection process through advanced artificial intelligence techniques. The proposed system integrates a wide range of data sources, including search history, showroom visits, car performance metrics, and social media engagements, to deliver highly personalized, accurate, and timely car recommendations that align with individual preferences, budgets, and lifestyles. The framework leverages multi-modal data processing to optimize car selection by analyzing both qualitative and quantitative user inputs, ensuring precise and context-aware recommendations. By addressing challenges such as choice overload and decision fatigue, the system enhances user decision-making capabilities. Real-time processing ensures that users receive up-to-date and relevant suggestions, improving engagement and overall satisfaction. The inclusion of a feedback loop further refines the system, enabling continuous improvement and adaptation to evolving user needs and market trends. This innovative framework represents a significant advancement in car recommendation systems by combining data diversity, state-of-the-art deep learning models, and real-time analytics. Its ability to provide dynamic, personalized, and actionable insights empowers users to make confident, well-informed decisions during their car-buying journey. With its robust and scalable design, this framework offers a transformative approach to modern automotive recommendation systems, setting a new standard for user-centric experiences.

关键词： Deep learning Measurement Feedback loop Accuracy Diversity reception Decision making Real-time systems Automobiles Regression tree analysis Recommender systems

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A Patient Response Prediction System using Multi-Scale Feature Fusion Semantic Segmentation Network and Med-YOLO

A Patient Response Prediction System using Multi-Scale Featu...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： S. Uma Shankari Hassan Mohamed Ali Boya Vijaya Durga M. Senthil Vadivu V. Mangaiyarkarasi Department of Computer Science and Applications SRM Institute of Science and Technology Ramapuram Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq School of Computer science and Engineering Malla Reddy Engineering College for Women (UGC-Autonomous) Maisammaguda Hyderabad India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Nowadays, most of the people are getting affected with various health issues such as typhoid, diabetes and cancer. A human immune system plays a crucial role in maintaining and balancing human health and reduces the risk of getting ill. Immunotherapy is a treatment which utilizes human body immunity power to fight cancer. There are many challenges in the existing models such as imbalance data, heterogenic data, uncertainties, and lack of temporal data. These challenges are resolved by imposing deep learning techniques and generating a valid information with the clinical details of patient health conditions. Therefore, a hybrid model name called Multi-Scale Feature Fusion Semantic Segmentation Network (MSSNet) and You Look Only Once (MedYOLO) for feature extraction and detection. Initially, Synthetic Minority Over-sampling Technique (SMOTE) is employed to overcome the issue of imbalanced data; further MSSNet is imposed for extracting the optimal features and detected using MedYOLO. Also, to get detailed information and generate cohort, the data collected from GSC hospital with overall details that are essential to predict the patient response to immunotherapy in cancer treatment. In the end, MSSNet-MedYOLO obtained higher accuracy of 98.16% for an effective prediction of patient response which is better than existing Convolutional Neural Network (CNN).

关键词： Semantic segmentation Cancer treatment Immunotherapy Predictive models Feature extraction Data models Data mining Convolutional neural networks Cancer Immune system

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Performance Analysis of 3D Stacked Memory Architectures in High Performance Computing

Performance Analysis of 3D Stacked Memory Architectures in H...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Venkat Tulasi Krishna Gannavaram Arun Kumar Gajula School of Electrical Computer and Energy Engineering Arizona State University Tempe USA Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The increasing speed of the CPU is greater than that of the RAM. The ‘Memory Wall’ issue, which occurs when the processor runs out of data, could occur. The I/O infrastructure is strained as the number of cores in CMPs rises because more data is needed from the memory subsystem. Memory bandwidth becomes a bottleneck in terms of performance. By layering memories on top of logic, three-dimensional integrated circuits are proposed as a solution to this problem. The widely-used compute-in-memory benchmark framework ‘NeuroSim’ is utilized by us to investigate the benefits of a three-dimensional design for a Neural Network application.

关键词： Three-dimensional displays Neural networks Memory architecture Random access memory In-memory computing Hardware Planning

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An Efficient Big Data Gathering in Wireless Sensor Network using Reconfigurable Node Distribution Algorithm 4

An Efficient Big Data Gathering in Wireless Sensor Network u...

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4th International Conference on Cognitive Computing and Information Processing, CCIP 2022

作者： Mallikarjunaswamy, S. Basavaraju, N.M. Sharmila, N. Mahendra, H.N. Pooja, S. Deepak, B.L. JSS Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India JSS Academy of Technical Education Bengaluru India JSS Science and Technology University Mysuru India K S Insitute of Technoloy Dept of Electronics and Communication Engineering Bengaluru India R.L.Jalappa Institute of Technology Dept of Computer Science and Engineering Kodigehalli India

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456487

In recent days, communication process is performed in wireless sensor networks (WSNs). The WSN is effectively reconstructed and accurately communicates the information to each corner of high-density nodes of cellular area. The main objective of this paper is to improve the data gathering of all sensor nodes and efficiently optimize the energy utilisation of each node in WSNs. The cluster head is identified in highly node distributed WSNs. The sink nodes during big data gathering are effectively utilized in WSNs with the help of Reconfigurable Node Distribution Algorithm (RNDA). The proposed algorithm procedure has been followed to address the big data gathering location in WSNs and mobilize the sink nodes in optimized location for proper communication in WSNs. The performance analysis and comparison between proposed and existing methods have been carried out with respect to energy efficiency, packet delivery ratio, packet ratio and transmission energy. The simulation result shows that the proposed method reduces energy consumption by 2 % in high density sensor node network communication process. The proposed method effectively selects the cluster head in WSNs to enhance the throughput packet delivery ratio and transmission energy. © 2022 IEEE.

关键词： Big data

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Impact of IoT Data Integration on Real-Time Analytics for Smart City Management

Impact of IoT Data Integration on Real-Time Analytics for Sm...

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International Conference on communications and Signal Processing

作者： D. Lakshmi J. Jeyarani R. Suguna P. Muneeshwari G.Mary Valantina Suganya Jayaraman Department of Computer Science and Engineering Panimalar Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering CARE College of Engineering Trichy Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The paper in illustration will emphasize the necessity for an IoT big data and analytics on time fusion as a futuristic smart city management approach. The framework deals with the main problems observed in data assimilation, analytics, security and scalability. As a result, cities will enjoy the services and operations utilization of the urban data with the precision. Our approach is based upon the data collection with the use of the edge technologies, preprocessing, integration, and analysis of the data resulting in the creation of the tools of machine learning and the adoption of interoperability standards. The results have especially brought to the limelight the fantastic performance of the live analytics pipeline when considered as a whole, and the impressive throughput of 5,000 events per second alongside the very low latency of 50 milliseconds, indicating how the urban information flow is processed in the blink of an eye. With AI therein creating the precision rate of 88.7%-95.2%, which therefore means this level of an urban situation can pronounce a traffic congestion and air quality fluctuations. The situation where security systems that involve encryption, access controls, and privacy-preserving protocols, among other mechanisms, have been used for the management of urban data in a safe manner, away from any unauthorized people and hackers, is factual. For the sake of achieving scalability, the replacement of certain bottlenecks is applied in the system by means of horizontal scaling and optimal performance procedures assisting smart cities handle their increasing quantity of data. Usually the results are factorized to bring out the fact that the particular methodology is strong and thus can be used in real practice to transform and enhance urban resilience level.

关键词： Smart cities Scalability Pipelines Machine learning Transforms Throughput Real-time systems

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Efficient Application of Adaptive Gaussian Kernel for Density Map Generation With Drone Data

Efficient Application of Adaptive Gaussian Kernel for Densit...

引用

Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Laxman Singh Khangarot Vyomika Singh Dharmendra Singh Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Electronics and Communication Engineering Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Acquiring Crowd information using Convolutional Neural Network techniques evolved promptly. The use of drones has also increased in recent years for gathering crowd information and analysis. These algorithms or CNN models require datasets that have the ground truth available for training and testing purposes. Among the various popular crowd datasets, most datasets are captured from still street cameras or use a high building point to acquire crowd imagery. In this paper, authors introduce a new “DroneVision” crowd Dataset captured using a drone for crowd counting and mapping. Further, the lower density crowd datasets use fixed sizes Gaussian kernel to generate density maps. While the dense crowd datasets use an adaptive Gaussian kernel as the head size varies based on distance and location context of the image. Drone data is captured at various heights and distances with various crowd densities therefore suitable adaptive Gaussian kernel needs to be identified for this dataset. In this study, the authors analyze and optimize the adaptive Gaussian kernel for the generation of a density map for the introduced dataset.

关键词： Training Adaptation models Head Video sequences Neural networks Lighting Convolutional neural networks Kernel Drones Testing

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An Effective Network communication algorithm for Dynamic Handover 3

An Effective Network Communication algorithm for Dynamic Han...

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3rd International Conference on Smart Technologies in Computing, Electrical and electronics, ICSTCEE 2022

作者： Sivasankaran, V. Maheswar, R. Sugumaran, S. Sountharrajan, S. Jayarajan, P. School of Electrical and Electronics Engineering VIT Bhopal University Bhopal India KPR Institute of Engineering and Technology Department of Electronics and Communication Enegineering Coimbatore India Sreenivasa Institute of Technology and Management Stuides Department of Electronics and Communication Enegineering Chittoor India Amrita School of Computing Chennai Amrita Viswa Department of Computer Science Engineering Vidyapeetham India Sri Krishna College of Technology Department of Electronics and Communication Enegineering Coimbator India

ISBN: (纸本)9781665456647

Today many devices like laptops and smartphones have access to both WIFI and LTE at the same time. The reliability and performance of wireless networks and the Internet is not perfect always. Sometimes users need to manually disconnect devices from slow or unreliable WIFI networks, in order to switch to LTE. when the connected WIFI network becomes unstable The Android operating system already includes a way to automatic switch to LTE,. In this research work, addressed the issues of switching between WIFI and LTE in Android. It is done by emulating various network conditions in order to provoke an Android phone to switch from WIFI to LTE. We show that the Android phone sometimes can stay connected to WIFI network networks even when there is very poor Internet access. The proposed algorithm shows the consistent in switching to LTE from poorly performing networks WIFI, however it also leads to much higher overhead in terms of data usage. © 2022 IEEE.

关键词： Android (operating system)

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LiFiData: A Secured Data communication over Defense Fields by using Intelligent Visible Light Assisted Transmissions

LiFiData: A Secured Data Communication over Defense Fields b...

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2023 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2023

作者： Chithra, L. Padma, S. Farzhana, I. Saravanan, L. Krishnakumari, S. S.A.Engineering College Department of ECE Chennai India Sona College of Technology Department of EEE Salem India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India PERI Institute of Technology Department of ECE Chennai India Loyola ICAM College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350319200

The electronic industry is currently experiencing a transformation due to the rise of solid state devices. Light-Emitting-Diode (LED) are the quintessential solid-state gadget example. LEDs' are a viable alternative to both incandescent and fluorescent lighting. They produce little to no heat and can save you money. As an added bonus, LEDs have a far longer lifespan than conventional light bulbs. LEDs generate higher-quality light in terms of color temperature than both incandescent and fluorescent bulbs. In addition, they can rapidly cycle across a wide range of light intensities. This system must be safe, cheap, and environmentally benign in order to successfully initiate electronic communication utilizing visible light. The suggested system uses a Arduino microcontroller in the transmitting portion, where a high-brightness LED light serves as the channel for communication and a transistor, BC 547, is employed to transform 5v to *** a microcontroller receives data from a computer, it decrypts the information by first converting it to binary, and only then sending it to the led as a digital pulse. The lightbegins to blink as data is transmitted to the receiver;the photo diode begins to sense light, generating electric pulses;the amplifier processes these pulses into data;the data reaches the Arduino, where it can be viewed in the program's serial monitor. For this transmission, direct line-of-sight is essential;however interference from other sources can improve the results in a dark environment. In the lab, LiFi has reached very high speeds, and it also outperforms Wi-Fi in terms of bandwidth, efficiency, availability, and security. This study provides a clear, experimental perspective on the benefits of LiFi technology as well as its potential future applications. However, the hybrid model that incorporates the WiFi and LiFi technologies is expected to see significant growth in demand in the telecommunications industry. © 2023 IEEE.

关键词： communication LED LiFi Security Visible Light Data Transmission VLC WiFi Wireless communication

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Edge Amplification in Cellphone and Gaze Unlicensed Bands 5

Edge Amplification in Cellphone and Gaze Unlicensed Bands

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

作者： Kavitha, M.S. Vashisht, Nitish Kalele, Girish Vigenesh, M. Kannimuthu, S. Katikar, Shilpa M. R.M.K.Engineering College Electronics and Communication Engineering Kavaraipettai Chennai India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Information Technology Coimbatore India Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350376685

There is significant possibility to use bandwidth (in the 28 - 38 GHz bands) and indeed the 60 GHz band for mitochondrial and classmates' wireless technologies as the price of enormously WiFi processors at micrometer wave (nanometers) rates continues to fall. Using thruster cell towers and a wireless service rolling based on the measured path particular input at bandwidth of 38 GHz and 60 GHz, this research provides dimensions of urban cytoskeleton and participant multi - band tv stations. The transmit delay distributed and insertion loss as a role of divorce maximum range and base station referring directions for a variety of real-life settings. According to the statistics provided here, carriers have significant dynamic routing delay distributions and can take advantage of a wide range of aiming angles to just provide dispersal connections at 38 GHz, where unimpeded lines of follow free space antenna radiation loss. At 60 GHz, there is noticeably more route loss, less variation in lag patterns, and fewer special station orientations for establishing links. We segments known correlations between RMS interruption spread and aerial aiming angles in both the 38 GHz and 60 GHz bands. We also note that extra route loss (above empty space) has a negative correlation with transponder spacing. © 2024 IEEE.

关键词： Micrometers

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Digital Twin-based Flight Dynamics Simulation

Digital Twin-based Flight Dynamics Simulation

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engineering Informatics, IEEE

作者： Geetam Talluri Anand Konjengbam Dept of Electrical Electronics & Communication Engineering Indian Institute of Technology Dharwad Dharwad India Computer Science & Engineering Indian Institute of Technology Dharwad Dharwad India

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

Unmanned Aerial Vehicle (UAV) systems face significant challenges during critical flight phases, such as post-liftoff stabilization and mid-flight disturbance response. This paper presents a Virtual Reality (VR) based simulation and control system integrated with a digital twin to enhance the understanding of UAV dynamics. The system uses real-time data from the UAV’s Onboard computer (OBC) to create a dynamic 3D environment where users can interact with flight parameters such as position, rotational dynamics, and vibration amplitude. This approach enables the analysis of UAV performance and stability under changing conditions by allowing real-time adjustments. This immersive system supports research, training, and operational optimization of aerospace systems.

关键词： Vibrations Three-dimensional displays Data visualization Virtual reality Autonomous aerial vehicles Aerodynamics Real-time systems Digital twins Vehicle dynamics Optimization

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