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Comparing Collaborative Filtering Algorithms in AI-Powered Recommendation Systems for E-Commerce 4

Comparing Collaborative Filtering Algorithms in AI-Powered R...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Aamer, Najmuddin Mittal, Tanusha Muthusamy, Nantha Kumar Battula, R. S. S. Raju Ajith, P.S. Soudagar, ManzooreElahi M. Bharat Ratna Indira Gandhi College of Engineering Department of Computer Science and Engineering Maharashtra Solapur410003 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Maharashtra Dehradun410003 India OntrailInc Dba Uprise San FranciscoCA94104 United States Aditya University Department of Computer Applications Andhra Pradesh Surampalem533432 India Brindavan College of Engineering Department of Mathematics Karnataka Bengaluru560063 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331523923

The quick growth of e-commerce has brought attention to how important efficient recommendation systems are to enhancing user experience and accomplishing business objectives. This paper investigates the effectiveness of collaborative filtering algorithms in AI-powered recommendation systems, with a focus on user-based collaborative filtering (UBCF). A comprehensive method is employed to guarantee consistency across different rating scales, beginning with data normalisation to standardise user-item interaction matrices. To decrease dimensionality and maintain the most significant patterns of user-item interaction, Principal Component Analysis (PCA) is employed for feature selection. The classification system uses similarity measures and UBCF to predict user preferences based on historical data. The algorithms are evaluated on key metrics like precision, recall, and RMSE using publically available datasets. The outcomes demonstrate the impact of preprocessing and dimensionality reduction on UBCF's performance as well as its efficacy in providing customised recommendations. This research contributes to the improvement of collaborative filtering algorithms for e-commerce systems, which will raise customer satisfaction and enable scalability in dynamic online markets. © 2025 IEEE.

关键词： Collaborative filtering

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Machine Learning Based ADMET Prediction in Drug Discovery 4

Machine Learning Based ADMET Prediction in Drug Discovery

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Bhavitha Lekshmi Prasad, P.S. Ani, R. Deepa, O.S. Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham coimbatore India

ISBN: (纸本)9798350305258

A drug is a substance or compound with the ability to treat or prevent disease in people or animals. Drug discovery is the process of identifying prospective medications for certain ailments, and it can take several years to accomplish. However, the introduction of machine learning has simplified the procedure. Virtual screening's major goal is to minimize the virtual compound space of biological molecules. To do this, we developed the VIRTUAL SCREENING TOOL, a virtual molecular descriptor calculator that can analyze molecules and assess their drug-likeness based on specific rules. We used Django with PyCharm, a Python IDE, to create a molecular description calculator, fingerprint generator, and prediction interface. The tool begins by inputting SMILES, which is the chemical structure of the molecule, and outputs molecular descriptors, which are the properties of the molecules, including their physicochemical properties, absorption, distribution, metabolism, lipophilicity, excretion, toxicity, drug-likeness rules, and fingerprint generation. The outputs are available in CSV format for download. © 2023 IEEE.

关键词： Drug Fingerprint Physiochemical property Prediction SMILES

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Blockchain for 5G-Enabled IoHT—A Framework for Secure Healthcare Automation

Blockchain for 5G-Enabled IoHT—A Framework for Secure Healt...

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International Conference on Data Analytics and Management, ICDAM 2022

作者： Alam, Md Imran Ahmad, Md Oqail Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Haneef Qidwai, Khalid Ali Department of Computer and Network Engineering Jazan University Jazan45142 Saudi Arabia Department of Computer Applications B.S Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai600048 India Department of Computer Science Jazan University Jazan45142 Saudi Arabia

ISBN: (纸本)9789811976148

Medical care has evolved into a lifeline for many people;due to this, there is an explosion in the field of medical big data. Wearable technology based on the Internet of Things (IoT) is being used by healthcare professionals to speed up diagnosis and treatment. There are now numerous sensors and devices, which are connected to the Internet. Internet of HealthThings (IoHT) can be used in many different ways in healthcare. Sensors and remote-monitored medical devices can help patients get more accurate and efficient diagnostics. This paper focuses on healthcare automation using the Internet of Things (IoT)-enabled devices. IoT-driven healthcare models can keep patients healthy and safe, enabling doctors to provide better care. In addition, healthcare frameworks significantly reduce healthcare costs and improve performance. Blockchain systems are transparent and thus safe to store and share private information. Data collected by medical devices attached to the body is kept in a cloud server alongside relevant medical reports as part of the proposed framework. The use of blockchain and 5G technology means that patients’ data can be securely transmitted at high speeds and with rapid response times. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Multi-Objective Cuckoo Search Optimization Algorithm for Optimal Resource Allocation in Cloud Environment 3

Multi-Objective Cuckoo Search Optimization Algorithm for Opt...

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3rd International Conference for Emerging Technology, INCET 2022

作者： Saif, Mufeed Ahmed Naji Vasudha Niranjan, S K Murshed, Belal Abdullah Hezam Sri Jayachamarajendra College of Engineering Vtu Department of Computer Applications Mysore India University of Mysore Department of Studies in Computer Science Mysore India

ISBN: (纸本)9781665494991

Cloud technology refer to a pool of virtualized heterogeneous computational resources which are served to different customers as a service through the internet. These virtualized resources get provisioned to cloud users dynamically based on demand. however, dealing with resources in such a dynamic environment is still a challenging task. Deciding the way, the available cloud computational resources get allocated to the workload requires efficient provisioning and allocation techniques. The clients need to operate their various applications on cloud servers with variant Quality of Service (QoS) requirements based on the workloads and type of application whereas the objective of the cloud providers is effectively reducing the energy consumption by optimizing the CPU and bandwidth utilization and maintaining the Service Level Agreement (SLA). This, however, makes resource provisioning and allocation a very complicated task. This article implements the meta-heuristic Multi-Objective Cuckoo Search Algorithm (MOCSA) for efficient resource allocation to dynamic upcoming user workloads and scheduling user tasks on cloud VMs, which aim at minimizing the energy consumption with efficient utilization of CPU and bandwidth. The implementation and simulation experiments were conducted on the Cloudsim simulation toolkit. The evaluation results proved that MOCSA performed well in terms of performance, energy consumption, CPU utilization, bandwidth utilization, and SLA violation. © 2022 IEEE.

关键词： Resource allocation

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LSEA-IOMT: On the Implementation of Lightweight Symmetric Encryption Algorithm for Internet of Medical Things (IoMT) 7th

LSEA-IOMT: On the Implementation of Lightweight Symmetric En...

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7th International Conference on Emerging applications of Information Technology, EAIT 2022

作者： Saif, Sohail Das, Priya Biswas, Suparna Department of Computer Applications Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India Department of Computer Science Chakdaha College West Bengal Chakdaha India Department of Computer Science & Engineering Maulana Abul Kalam Azad University of Technology West Bengal Kolkata India

ISBN: (纸本)9789811951909

The Internet of Medical Things (IoMT) has made a monumental improvement in patient monitoring, early detection of diseases and treatment methods. This not only enriched the quality of living but also the healthcare cost and errors are minimized. This promising technology has greatly helped the patient and frontline healthcare workers. However, security and privacy breaches are one of the major concerns in IoMT due to wide variety of devices and communication network. The lack of security can affect a patient life since IoMT devices transmit patient data to a cloud-based medical server over insecure channel. Hence, maintaining the privacy and confidentiality becomes an utmost issue worth of further exploration and resolution. Cryptography is one of the primitive technologies which can ensure the confidentiality of a data;however, the complex solutions are not a good choice for resource-constrained IoMT device. Accordingly, we have designed a novel lightweight symmetric encryption algorithm (LSEA) for low-powered IoMT devices. Proposed algorithm uses 8-bit block ciphering technique using two 8-bit secret keys. Physiological parameter such as pulse rate of human body has been considered as one the secret keys while designing the algorithm. Security analysis shows that the algorithm is resilient to known plain text attack and known cipher text attack. LSEA has been tested in Arduino with varying payload. Experimental result depicts that it requires only 1.25 ms time for 16 byte and 7.4 ms for 512 byte data. Proposed algorithm has been compared to AES in a similar experimental environment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Optimizing Resource Allocation in Mobile Edge Computing for IIoT through Reinforcement Learning and Multiobjective Particle Swarm Optimization 2

Optimizing Resource Allocation in Mobile Edge Computing for ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Avv, Sudhakar Mohan, M. Maharajan, K. Habelalmateen, Mohammed I. Naga Saranya, N. SR University Department of Electrical and Electronics Engineering Telangana India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Bangalore India Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques - more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms - are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32. © 2024 IEEE.

关键词： Communication Industrial Internet of Things Mobile Edge Computing Optimization Quality of Service Reinforcement Learning

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Deep Learning based Smart American Sign Language Detection in Alphabets 6

Deep Learning based Smart American Sign Language Detection i...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： RukumaniKhandhan, C. Vijayakumar, P. Sri Sabari, C. Arun, G. Chandrasekaran, N. Rohith Bhat, C. Kongu Engineering College Department of Computer Technology - PG Erode Perundurai India Kongu Engineering College Department of Master of Computer Applications Tamil Nadu Perundurai India Kongu Engineering College Department of Computer Technology - PG Tamil Nadu Perundurai India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thuduppathi India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350304480

Disabled people are of different kinds like physically challenged people, mentally challenged etc. To help the people who cannot convey their thoughts and who cannot hear others thoughts, sign language is being used to communicate with them. Sign language is something that is used to communicate through body movements mainly through hands and *** Sign Language(ASL) is an authorized sign language that is taught and used widely. ASL is difficult to teach and learn for them because it is completely new and they have no knowledge about it. In this research paper the ASL is recognized and converted into text through data mining techniques. The study uses a dataset of ASL alphabet images and applies various data mining techniques, including feature selection, classification, and clustering. The features extracted from the images include shape, size, color, and texture. The use of neural networks, decision trees, and support vector machines speeds up the data categorization process. Techniques such as k-means clustering and hierarchical grouping are used to facilitate entity grouping. The results show that the best performance is achieved using a combination of feature selection and classification techniques. The decision tree algorithm with the selected features achieves an accuracy of 96.5% on the test dataset. The system can accurately recognize all the ASL alphabets and convert them into text or speech. The system developed can be used to improve communication between deaf and hearing individuals and can be extended to recognize other ASL gestures and phrases. Future research can focus on real-time detection and recognition of ASL gestures using data mining techniques. CNN algorithm is used to recognize the image based on the images collected. The accuracy rate gets low and the detection is inaccurate. Therefore, VGG16 algorithm is used to recognize the images and it is unique in convolutional models. © 2023 IEEE.

关键词： ASL CNN Gestures VGG16

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Predicting Rainfall Using an Artificial Neural Network-Based Model 6

Predicting Rainfall Using an Artificial Neural Network-Based...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Shukla, Aasheesh Sharma, Manish Tiwari, Kripanshu Vani, V Divya Kumar, Naveen Pooja GLA University Computer Engineering and Applications Uttar Pradesh Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

Rainfall estimation poses a significant and intricate environmental challenge in today's context. Achieving accurate forecasts often necessitates the deployment of sophisticated machine models and simulations because weather patterns and rainfall dynamics are typically highly nonlinear and constantly changing. In India, where agriculture serves as the cornerstone of the economy, special emphasis is placed on the relationship between agricultural productivity and rainfall. Farmers rely on rainfall forecasts to gauge potential crop yields. Precipitation forecasting leverages scientific and technological advancements to assess atmospheric conditions. Accurate monitoring of rainfall is pivotal for optimal crop growth, efficient water resource management, and strategic planning of water systems. To emulate the behavior of such intricate nonlinear systems, artificial neural networks (ANN s) have proven to be a valuable tool. Over the years, numerous researchers in this field have effectively harnessed ANN s. This research highlights several approaches employed by various researchers to predict rainfall using ANN s. This proposal underscores that forecasts based on ANN s outperform those derived from conventional mathematical and numerical methods, offering greater precision in rainfall predictions. © 2023 IEEE.

关键词： Accuracy Agriculture Artificial Neural Network Crop Productivity Prediction Rainfall

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Cloud-based Data Analytics for Automated Coastal Cleanup Robots with Convolutional Neural Network 8

Cloud-based Data Analytics for Automated Coastal Cleanup Rob...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Babu, A. Ramesh Basavaraddi, Chethan Chandra S. Ramesh, S. Mouleeswaran, S.K. Sahoo, Santosh Kumar Solainayagi, P. Vidya Vikas Institute of Engineering and Technology School of Computer Applications Karnataka Mysore India R&D Don Bosco Institute of Technology Bangalore Vtu Department of Artificial Intelligence and Machine Learning Karnataka Belagavi India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Chennai India Vit University Department of Engineering and Technology Madhya Pradesh Bhopal India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Bangalore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

The growing danger of coastal pollution requires novel, automated remediation methods. This paper proposes combining autonomous robots with sophisticated sensor technologies and a Convolutional Neural Network (CNN)-based data analytics system to clean up coastal areas. The robots autonomously cruise coastal settings, discover and categories marine waste using onboard sensors, and send data to a cloud-based infrastructure for analysis. The proposed method relies on CNN, a deep learning model known for image recognition. For reliable identification and classification, CNN is trained on a broad collection of marine waste images. The cloud-based data analytics platform receives, processes, and stores cleaning robot data. It handles massive datasets effectively using cloud computing scalability and processing power. The technology uses real-time cloud image processing to identify garbage and enable robots to adjust their cleaning tactics. The data analytics platform generates detailed reports and visualizations to analyze cleaning efficiency and environmental effect. It advances automated coastal cleaning technology by integrating advanced robotics, sensor technologies, and deep learning. The proposed approach improves coastal cleaning speed, accuracy, and flexibility to protect marine habitats and biodiversity. © 2024 IEEE.

关键词： Cloud-based Infrastructure Environmental Monitoring Image Recognition Real-time Processing Sensor Technologies

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