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Smart Electronics and Communication (ICOSEC), International Conference on

作者： M. Sangeetha S.Rajes Kannan Sampath Boopathi J Ramya Mohammad Ishrat G. Sabarinathan Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer science and Engineering Chennai institute of Technology Chennai Tamil Nadu India Department of Mechanical Engineering Muthayammal Engineering College Rasipuram Tamil Nadu India Department of Computer Science and Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Mathematics PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. The suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. The random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset.

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An Investigation of IOT-Based Consumer Analytics to Assist Consumer Engagement Strategies in Evolving Markets 3

An Investigation of IOT-Based Consumer Analytics to Assist C...

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

作者： Jayadeva, Sujay Mugaloremutt Prasad Krishnam, Nagendra Raja Mannar, B. Prakash Dabral, Amar Buddhi, Dharam Garg, Neha Jss Academy of Higher Education & Research Department of Health System Management Studies Faculty of Management Studies India Telangana Hyderabad India St. Theresa Intl College Business Administration Thailand Graphic Era Deemed to Be University Department of Hospitality Management Uttarakhand Dehradun India Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

In order to establish sustainable operations, Internet innovation has been widely adopted in the retail sector;however, certain operational difficulties, such as security and confidentiality have been uncovered in practise. As a result, linked fields must do more thorough theoretical study. In order to compete with online merchants and promote sustainable growth, this study examines how traditional brick and mortar stores might use IoT technology. First, by taking into account the costs of customer inconvenience associated with shopping through various retail partners and the influence of technology, pricing Plans were created for the store and the retailer of concrete blocks, respectively. Furthermore, by contrasting different scenarios, the benefits of internet to retail shops and consumer excess are analysed. After that, the basic models were expanded to take into account consumer - perceived sensitivity regarding IoT technology and price competitiveness in order to test the robustness of the analytical conclusions. Additionally, actual data-based numerical analyses are performed. © 2023 IEEE.

关键词： Internet of things

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Smart Retail Store Surveillance and Security with Cloud-Powered Video Analytics and Transfer Learning Algorithms

Smart Retail Store Surveillance and Security with Cloud-Powe...

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Intelligent Cyber Physical systems and Internet of Things (ICoICI), International Conference on

作者： P. Sivalakshmi U. Kavitha R. Usha Ojasvi Pattanaik S.P. Maniraj C. Srinivasan Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India School of Commerce Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vignan Institute of Management and Technology for Women Ghatkesar JNTU Hyderabad India Department of Data Science and Business Systems Faculty of Engineering & Technology SRM Institute of Science and Technology Kattankulathur 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

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveillance by combining transfer learning (TL) algorithms with cloud-powered video analytics. It is now possible to remotely interpret, analyze, and store massive amounts of video data produced by security cameras using cloud computing capabilities. In addition, TL methods are used to customize pre-trained deep learning models for retail settings, allowing for precise event and anomaly identification. This method drastically reduces the computing load on local devices and makes surveillance systems more accurate and responsive. One of the main advantages of the proposed system is that it can monitor the store premises in real time. It can also automatically identify suspicious actions like stealing or damage and inform staff or security about possible risks. By using cloud infrastructure, the system can also adapt to changing security needs and expand dynamically to handle increasing data volumes. Retailers may find useful information for refining shop layouts, product placements, and marketing tactics by integrating cloud-powered video analytics, which allows advanced functions like crowd analysis, heat mapping, and customer behavior analysis. A thorough plan for smart retail store security and surveillance, using cloud computing, video analytics, and TL algorithms to build a strong and effective system for protecting digital retail spaces.

关键词： Cloud computing Machine learning algorithms Accuracy Surveillance Visual analytics Scalability Transfer learning Real-time systems Safety Security

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding

Robust Spectral Efficiency Maximization in 5G Networks Using...

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Disruptive Technologies (ICDT), International Conference on

作者： Geetha Ponnaian Jayanthi. N R. Sarasu Sajitha. L.P R. Ramalakshmi Manimegalai L Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and business system R. M. K Engineering College Kavaraipettai Tamilnadu India Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios.

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Attention Field Size Alters Patterns of Population Receptive Fields in the Early Visual Cortex

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Neuroscience Bulletin 2022年第2期38卷 205-208页

作者： Bo Liu Xiaochun Wang Le Wang Qiaojun Qu Wei Zhang Bin Wang Jie Xiang Ting Yan Tianyi Yan Hui Zhang College of Medical Imaging Shanxi Medical UniversityTaiyuan 030001China Department of Radiology First Clinical Medical CollegeShanxi Medical UniversityTaiyuan 030001China College of Information and Computer Taiyuan University of TechnologyTaiyuan 030001China Intelligent Imaging Big Data and Functional Nano-imaging Engineering Research Center of Shanxi Province Shanxi Medical UniversityTaiyuan 030001China Translational Medicine Research Center Shanxi Medical UniversityTaiyuan 030001China School of Life Science Beijing Institute of TechnologyBeijing 100081China Ministry of Industry and Information Technology Key Laboratory of Convergence Medical Engineering System and Healthcare TechnologyBeijing Institute of TechnologyBeijing 100081China

Dear Editor,Visual spatial attention is a cognitive process by which prior information about the relevance of spatial locations is used to improve perceptual performance[1].Visual attention is spread across the attention field(AF),a range within visual *** AF reshapes the distribution of activity throughout the visual pathway,including the early visual cortex(EVC)and all areas along the dorsal and ventral visual cortical pathways[2,3].Importantly,the effect of attention depends not only on the spatial position of the AF but also on its size[4].

关键词： Visual Cortex visual

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The effect of data Augmentation Using SMOTE: Diabetes Prediction by Machine Learning Techniques 23

The effect of Data Augmentation Using SMOTE: Diabetes Predic...

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Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing Conference

作者： A. Al-Qerem A. M Ali M. Alauthman M. Al Khaldy A. Aldweesh Computer Science Department Faculty of Information Technology Zarqa University Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Jordan Department of Information Security Faculty of Information Technology University of Petra Jordan Business Inteligence & Data Analytics University of Petra Jordan College of Computing and Information Technology Shaqra University Saudi Arabia

ISBN: (纸本)9798400716225

Diabetes mellitus, a severe and enduring condition characterized by impaired glucose metabolism, poses a substantial threat to public health. Its pervasive impact continues to escalate globally, with a rising incidence that challenges preventive measures. Despite earnest efforts, individuals struggle to evade the clutches of diabetes, necessitating innovative approaches for disease management. Traditional methodologies in diabetes health monitoring exhibit limitations, prompting the exploration of advanced techniques. This study employs machine learning (ML) methods to delve into diabetes, aiming to enhance diagnostic accuracy. The primary objective is to develop a method capable of precise diabetes diagnoses with a heightened level of precision. The investigation incorporates machine learning algorithms, specifically Random Forest (RF), K Nearest Neighbor (KNN), and Logistic Regression. The inclusion of these algorithms seeks to streamline data processing ***, this study incorporates the Synthetic Minority Over-sampling Technique (SMOTE) as a data augmentation strategy. SMOTE addresses imbalances in the dataset, contributing to a more robust and representative sample. The research evaluates the effectiveness and accuracy of diabetes prediction using these algorithms both before and after SMOTE implementation. By considering the impact of SMOTE, the study aims to determine the optimal algorithm for assessing diabetes development. The comparative analysis sheds light on how SMOTE enhances the overall performance of machine learning models. This nuanced approach not only refines diabetes diagnostic protocols but also underscores the significance of addressing data imbalances in predictive modeling for enhanced precision in disease prediction.

关键词： Classification data augmentation Diabetes

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V Revathi Balasubramanian Prabhu Kavin Arunadevi Thirumalraj E. Gangadevi Balamurugan Balusamy Shilpa Gite Department of Applied Sciences - Physics New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India Department of Computer Science Engineering K.Ramakrishnan College of Technology Trichy Tamil Nadu Department of Computer Science Loyola College Chennai Delhi-National Capital Region (NCR) Shiv Nadar University AIML department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person’s diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN’s superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme’s relatively strong computational mo

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Machine Learning Development in Solving Critical Medical Problems

Machine Learning Development in Solving Critical Medical Pro...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Shaista Fatima G Sangeetha P Ponmurugan A. N. Arularasan A. Prabhu Chakkaravarthy R Denis A. Chinnasamy Department of Electronics and Communication Engineering MJ college Hyderabad Telangana India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Thiruvallur Tamil Nadu India Department of Research Meenakshi Academy of Higher Education and Research Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Networking and Communications School of Computing College of Engineering and Technology Kattankulathur Tamil Nadu India Department of Computer Science Mount Carmel College Autonomous Bengaluru Karnataka India Department of data science and business systems School of computing SRMIST Kattankulathur campus Tamil Nadu India

As a result of ML, the healthcare industry undergoes substantial innovation and improvement. As a result, data management, clinical operations, drug research, and surgery are all progressing more quickly. The healthcare sector is now required to use this cutting-edge technology because of the Covid-19 pandemic. More importantly, people stand to benefit the most from technology because it may improve their health outcomes by identifying the best treatment alternatives for them. Thanks to ML's enhanced early disease detection capabilities, the frequency of re-admissions to hospitals and clinics can be reduced. In this article, we'll look at the main applications of machine learning in healthcare as well as its exceptional benefits in changing the industry.

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Integrating Deep Learning and data science: Challenges, Opportunities and Implications

Integrating Deep Learning and Data Science: Challenges, Oppo...

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International Conference on Computing and Networking technology (ICCNT)

作者： Ankita Agarwal Nagaraj Patil M. Rajeswari Gitanjali B. Yadav Rahul Mishra Gourav Sood Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep gaining knowledge of and records science are the most important and surprisingly influential fields of technology. Deep studying can system extensive statistics to discover styles, create insights, and have their personal choices concerning further analysis. Facts and technological know-how are centered on information-pushed business selection-making, using predictive analytics, machine studying, and other more recent algorithms to perceive, refine, and increase intelligent choices based on information. Those fields are rapidly developing and overlapping, creating an ever-increasing demand for each technical professionals and people with an intensive knowledge of statistics science and deep mastering. Integrating deep gaining knowledge of and records technology can create some challenges and possibilities which have to be explored.

关键词： Deep learning Portable computers Accuracy data science Prediction algorithms Complexity theory data mining Predictive analytics business

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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