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AiML-Applications for engineering & technology (iCAEt), international Conference on

作者： Nilesh R. Kolhalkar Anupama A. Pandit shridhar Ashok Kedar G. Yedukondalu Department of Mechanical Engineering MKSSS's Cummins College of Engg. For Women Pune MH India Department of Computer Science & Engg. Defence Institute of Advanced Technology (D.I.A.T) Pune Pune MH India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Vijayawada A.P. India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Robotic harvesting of fruits and vegetables is an advanced technology that leverages Robotics, Artificial intelligence, and Machine Vision to harvest the fruits autonomously from plants or trees. this technology aims to address labor shortages, enhance efficiency, reduce costs, and minimize damage to the fruit during harvesting. Ai algorithms for fruit detection and harvesting are increasingly used in agricultural automation to improve efficiency and accuracy. the accuracy of detection algorithms in fruit detection and harvesting can differ reliant on various factors, including the type of algorithm used, the quality and diversity of the training data, the complexity of the environment, and the specific fruits being targeted. Advanced control algorithms integrated with image processing ensure that the robotic arm moves smoothly and accurately, minimizing the risk of bruising or damaging the fruit. soft robotics and adaptive gripping technologies are discussed in the paper which can handle delicate fruits like grapes, without applying excessive force. Machine vision integrated robot arm with novel gripper and cutter for harvesting cluster fruit like grapes is reported in the paper. Case studies of agricultural robots for Orchards, Greenhouses and Field Crops are discussed with detailed analysis along with challenges, future trends and innovations.

关键词： Accuracy Costs Automation Machine vision Pipelines Lighting training data Robot sensing systems Prediction algorithms Robots

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Current controlled charging scheme for off board electric vehicle batteries from solar PV array 7

Current controlled charging scheme for off board electric ve...

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7th international Conference on signal Processing and integrated Networks, sPiN 2020

作者： singh, Harshit Kumar Kumar, Nishant Dr. A.P.J Technical University Department of Electrical and Electronics Engineering I.T.S Engineering College Greater Noida201308 India Department of Electrical and Computer Engineering Singapore117583 Singapore

ISBN: (纸本)9781728154756

in this paper, we are proposing current-controlled hill-climbing strategy used in Maximum power point tracking (MPPt) of the solar photovoltaic array to Charge OFF Board Electric vehicle lithium-ion batteries within the safety limits and supported rating of batteries so that EV battery does not get damaged and can also increase the life of the battery. Here, a current sensor and potential difference sensor-based electric vehicle off-board battery charging by hill-climbing strategy is tested on MAtLAB simulation. the satisfactory steady-state battery is charging within the rated charging limit, under variable solar irradiance. © 2020 iEEE.

关键词： Electric vehicles

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An Advanced Cognitive Approach for Personalized Drug Formulation in type 2 Diabetes Using Deep Neural Networks and Feature-based Fusion

An Advanced Cognitive Approach for Personalized Drug Formula...

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2025 international Conference on intelligent Control, Computing and Communications, iC3 2025

作者： singh, Brijendra Pratap singh, Jagendra suryavanshi, Madhuri Jailani, s. Amer, A. Beer A. Kumar, Rakesh School of Computer Science Engineering & Technology Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India Alpha Pharma Kaec R&d Formulation Dev. Rabigh Saudi Arabia Sadat academy for management and Sciences Canob street Alexandria Egypt Magadh University Department of Computer Science and I.T. Bodh Gaya India

ISBN: (纸本)9798331527495

Personalized medicine is an innovative approach toward achieving improved therapeutic outcomes, notably in chronic diseases like type 2 Diabetes (t2D), where individualised drug formulations represent the crux of treatment. Conventional methods of drug prescription tend to focus on generic approaches without considering the specific genetic, physiological, and lifestyle characteristics of individuals. this research fills this gap by developing an advanced deep learning-based method for personalised drug formulation in t2D. it integrates diverse patient data, including demographics, medical history, clinical measurements, genomic information, pharmacokinetic information, real-time monitoring data, and responses to previous treatments. For optimal drug formulation, it makes use of a combination of deep learning models, such as Recurrent Neural Network (RNN) for time-series prediction, Convolutional Neural Network (CNN) for genomic analysis, GAN for producing synthetic data, and auto-encoder for feature extraction. the dataset consists of 2700 patient records collected over one year, wherein the pre-processing phase includes handling missing values, normalising continuous variables, and encoding categorical features. trained models were assessed based on precision, recall, F1-score, and AUC ROC levels;the best performance came from the RNN model with a maximum accuracy of 97.35%. CNN, GAN, and Autoencoder also obtained similar competitive results. However, the RNN-based model received the highest score in recommending the appropriate drugs. the application of this research can contribute to the broader discipline of precision medicine. © 2025 iEEE.

关键词： theranostics

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the impact of industrial Automation on the Manufacturing industry in the Era of industry 4.0

The Impact of Industrial Automation on the Manufacturing Ind...

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international Conference on Computing and Networking technology (iCCNt)

作者： Vidya sunil Gurav Anchit Gugnani Y R Meena Vijay Marathe s. Arul Antran Vijay simranjeet Nanda Department of Management Studies D.K.T.E.S's Textile and Engineering Institute Ichalkaranji Amity Institute Of Physiotherapy Amity University Jaipur Rajasthan Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka Vishwakarma Institute of Technology Pune Department of Computer Science Engineering Karpagam College of Engineering Coimbatore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

With the dawn of industry 4.0, industrial automation has become one of the most disruptive technologies in the manufacturing industry. it encompasses the use of innovative technology and methods to automate different processes related to production, which decreases dependence on human laborers and ensures increased production results with greater efficiency. it was a game changer for the industry - industrial Automation that transformed product design, production, and delivery. A significant positive aspect of using industrial Automation is that it improves the speed and accuracy at which production occurs. Robotization, artifidal intelligence, and modern equipment combined to allow manufacturing to continue mumuchquicker while being more accurate. it helps reduce production time, bringing down wastage and errors due to cost-saving, leading to better quality control. the integration resulted in industrial automation leading towards smart Factories where machines & systems are interconnected and communicate banks on each other. this has enabled the manufacturers to monitor and optimize their production processes, ensuring their organization planned maintenance over preventive repairs, avoiding machinery failures, or putting suppliers in check with plummeting demand. industrial Automation increasing Potential Job Displacement and Reskilling/Upskilling of Workers.

关键词： Manufacturing industries technological innovation Accuracy service robots supply chains social robots Production Real-time systems Fourth industrial Revolution smart manufacturing

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Challenges and innovations in Multimedia and Real-time Networking: A Review of Modeling Approaches

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Procedia computer science 2025年 252卷 53-62页

作者： Milind shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile internet access has led to the widespread adoption of real-time networking and multimedia applications. these platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. the demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COViD-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. this review paper explores into the basics and introduction of Multimedia and Real-time Networking. it also explores the advancements in Adaptive and intelligent Networking for real-time multimedia communication. the paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real time Networking Multimedia Challenges innovations intelligent Networking

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Optimizing Emotion Recognition through Weighted Averaging in Deep Learning Ensembles

Optimizing Emotion Recognition Through Weighted Averaging in...

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Communication, security and Artificial intelligence (iCCsAi), international Conference on

作者： Arun Kumar Dubey Achin Jain Arvind Panwar Manish Kumar Harsh taneja Puneet singh Lamba Bharati Vidyapeeth's College of Engineering New Delhi School of Computing Science Engineering Galgotias University Greater Noida UP India Graphic Era (Deemed to be) University Dehradun Uttarakhand India Department of Computer Science S.G.T.B Khalsa College University of Delhi Delhi India

Facial emotion recognition (FER) is a necessary in order to bridge the communication gap between people and computers since, in the complete range of face-to-face contact, facial expressions are a sort of communication that convey the majority of a person's emotional and mental state. this field's innovations also enhance how successfully computers interact with or assist people. the vast body of research in this field is focused on Deep Learning. FER has drawn interest from the research community due to its possible applications. the main objective of FER is to relate different facial expressions to the associated emotional states. Even though profile pictures from different angles are essential for a functional FER system, most current systems only take frontal photos into account, which is a severe drawback. By adapting a pre-trained DCNN model's dense top layer(s) to be FER-compatible, the model is employed. the model is then fine-tuned using emotion data, which eventually increased the FER accuracy to a larger degree. in this study, We have used ensemble method over VGG16 & VGG19. the suggested method is tested using the well-known datasets FER2013 and CKPLUs and two different DCNN models that have been pretrained (VGG16 and VGG19). the suggested technique had an accuracy of 90.38% on FER2013 and 96.53% on CKPLUs after ensemble learning.

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Development of Context-sensitive Formulas to Obtain Constant Luminance Perception

Development of Context-Sensitive Formulas to Obtain Constant...

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iEEE signal Processing and Communications Applications (siU)

作者： Ergun Akleman Bekir tevfik Akgün Adil Alpkocak Visual Computing and Computational Media SVPA Texas A & M University Computer Science and Engineering College of Engineering Texas A & M University College Station ABD Bilgisayar ve Bilişim Bilimleri Fakültesi Yeditepe Üniversitesi İstanbul Türkiye Bilgisayar Mühendisliği Fakültesi İzmir Bakırçay Üniversitesi Menemen Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

in this article, we present a method to develop context-sensitive luminance correction formulas to produce constant luminance perception for a foreground object. Our formulas make the foreground object slightly transparent to mix with the blurred version of the background. this mix can be used to quickly produce any desired illusion of luminance in foreground objects based on the luminance of the background. the transparency formula has only one variable; the relative size of the foreground object. We identified the general structure of the transparency formula as a function of the relative size of the foreground object. We develop a method to intuitively control the coefficients of the transparency formula. We have implemented an interactive Web-based program in shadertoy to identify coefficients. Using this program, we determined the coefficients of the transparency formula. We also identified a simpler affine formula, which requires only two coefficients. We made our program publicly available so that anyone can access and improve it. in this article, we also explain how to intuitively change the polynomial part of the formula. Using our explanation, users change the polynomial part of the formula to obtain their own perceptively constant luminance. this can be used as a crowd-sourcing experiment to further improve the formula.

关键词： Visualization signal processing Polynomials Object recognition

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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Pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-tai Chiu, Yi-Hung Ouyang, Chen-sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and statistical Manual of Mental Disorders, Fifth Edition. the diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: in this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. this study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. the variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: the results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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transforming Hospital Visitor screening with Cloud-Powered Deep Learning for safety and security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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iEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： D. Antony Joseph Rajan sivanesan t M N. Mohankumar M. srinivasan Audithan sivaraman s. Velmurugan 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 Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

this research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. this rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. it presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. the proposed method greatly improves hospital security since it might increase safety and reduce dangers.

关键词： Deep learning Cloud computing image recognition Accuracy Hospitals threat assessment Real-time systems security Convolutional neural networks testing

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Quantitative Evaluation to Detect Crack Depth in Beams Based on CNN-RNN-LstM Approach

Quantitative Evaluation to Detect Crack Depth in Beams Based...

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self sustainable Artificial intelligence systems (iCssAs), international Conference on

作者： Mohammad Asif Gandhi A. N. swaminathen Deepak t. Patil A Ravitheja R. Kamali Akshay Rajput Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Panvel Maharashtra India Adi Shankara Institute of Engineering and Technology Kerala India Bharati Vidyapeeth college of Architecture Navi Mumbai Maharashtra India Department of Civil Engineering SVR Engineering College Kurnool Andhra Pradesh India Department of Computer Science And Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

in this study, the proposed approach offers a novel hybrid image-based deep learning method for autonomous concrete fracture identification. Cracks are easier to spot and false positives are reduced with hybrid images that include visual and infrared thermography data. in specifically, a Visible and infrared camera as well as a line laser make up a hybrid imaging system for spatially scanning unmanned vehicles can efficiently assess large-scale concrete-made constructions like bridges and dams. Crack detection is a commonplace task in many industrial domains, however the current method of relying on expert judgment is typically laborious, time consuming, and inaccurate. the practical benefits of automated decision-making increase as the size of the target concrete construction grows. Cracks can be automatically identified and seen using the proposed method. Preprocessing, segmentation, feature extraction, and model training are the four components of the proposed method. it performs clever edge recognition, scaling, and greyscale conversion as part of its preparation. improved K-Means segmentation is used for the separation. it employs GLCM for feature extraction, and CNN-RNN-LstM for model training. the new method is compared to two established approaches, CNN-RNN and CNN-LstM, and is found to be superior.

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