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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Patra, Raj Kumar Patil, Bassamma Kumar, T. Sathish Shivakanth, Gandla Manjula, M.B. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India H. K. E. Society's S. L. N. College of Engineering Department of Electronics & Communication Engg Raichur India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India K L University Department of Computer Science & Engineering Vadeeshwaram Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Social networking platforms, online news outlets, and weblog hosting services continue to expand, and with them come an increasing number of user-generated content contributions such product evaluations, comments on recent articles, and more. Products, movies, shopping sites, and review sites are common areas for customer feedback. The sheer volume and rate of growth of material that expresses opinions is becoming a burden on manufacturers who must manually categorise this data. Also, the perspective on entities at the level of aspects is expected by the public. It is for this reason that an automated sentiment analyzer must be built, one that can detect the bipolar and multipolar sentiment polarity of documents and/or aspects. People's ability to voice their opinions openly in public has greatly increased with the advent of various social networking apps. As a result, this helps to further the field of automated emotional analysis by providing a wealth of data on which to base analyses of people's feelings. User review categorization and analysis has emerged as an important part of sentiment analysis in recent years. Opinion mining is used to determine the degree of positivity or negativity in each user review posted on a social network. Numbers, star ratings, and descriptive text are the three polarity indications in a review. The sentiments of the public have been analysed using a wide variety of machine learning methods, but these methods often fall short in key areas such as classification accuracy, precision, recall, and F-measure due to pre-existing classification problems such as the two-class problem, overfitting, and parallel processing. The primary goal of the study is to create a fully automated system that can analyse a massive dataset of movie reviews using aspect-based SA or OM. We use natural language processing to tally up the good, bad, and ugly reviews. The research enhances advertising efforts and guides customers to the most suitable products. I

关键词： Sentiment analysis

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Deep Learning Classifier to Recommend the Tourist Attraction in Smart Cities

Deep Learning Classifier to Recommend the Tourist Attraction...

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Disruptive technologies (ICDT), International Conference on

作者： Keshav Gupta Vikas Kumar Abhishek Jain Pranita Singh Amit Kumar Jain M.S.R. Prasad Department of Computer Science and Engineering GRD Institute of Management and Technology Dehradun India School of Computer science & Engineering Dev Bhoomi Uttrakhand University Dehradun India Department of Computer Science and Engineering Uttaranchal University Dehradun India Department of Computer Science and Engineering Roorkee Institute of Technology Roorkee India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India

Provide an attraction recommendation system that uses deep learning and is powered by the Internet of Things (IoT) to develop the smart city visitor experience. Users of a smart city app or website will be able to record information about their trips, including whether they are going independently or with an accompanying companion, the sort of companion (e.g., a family with children or a lover), the purpose of their trip (business or pleasure), and personal characteristics (e.g., age, interests, etc.). Using this unique combination of input data, the suggested deep learning-based recommendations system would suggest tourist activities and sights that match the user's profile nicely. In addition, the suggested tourist attraction recommendations system uses actual data obtained from Internet of Things (IoT) devices to offer content-based information (such as previously looked-at sights) and context related data (such as weather conditions, location, time of day, etc.) to potential visitors in the smart city. Regarding the initial case [(a) planning and searching for activities earlier travelling], the suggested multi-label deep learning classifiers achieve a recall, accuracy, precision, and loss of 1.6%, 98.6%, 98.8%, and 98.7%, respectively, compared to other models such as extra tree, decision tree, k-nearest neighbour, and randomized forest. As for the second scenario, it can effectively suggest tourist spots while searching for smart city activities (b) with a recall, accuracy, precision, and loss of 2.6%, 98.4%, 98.6%, and 98.7%, respectively.

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Preliminary Staff Collaboration and Artificial Intelligence 3

Preliminary Staff Collaboration and Artificial Intelligence

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3rd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2023

作者： Pandey, Arvind Kumar Mahesh, T.R. Christy, Veena Shaikh, Mohsin Yadav, Preksha Das, Saptorshi Arka Jain University Department of Computer Science Jharkhand Jamshedpur India Department of Computer Science and Engineering Bangalore India Bharat Institute of Higher Education and Research Department of Business Administration India Dr. Vishwanath Karad Mit World Peace University Department of School of Management Pune India Cmr Institute of Technology Master of Business Administration Bangalore India Kiit Deemed to Be University Kiit School of Management India

ISBN: (纸本)9798350399264

Artificial Intelligence is viewed as by a few contemporary associations all over the planet to foster able labor forces, where the ways of behaving and abilities of the workers are assisting those associations with acquiring upper hands. The administration experts of the contemporary associations are recognizing that the representatives can work cooperatively because of AI and prescient information examination. Utilization of Artificial Intelligence in the representative administration exercises is assisting a few contemporary firms with driving positive representative commitment as a result of the improvement of durable workplace. This exploration work is centered around the positive effect of Artificial Intelligence on representative commitment to this contemporary worldwide business *** models and hypothetical structures on worker commitment are applied in this review. Then again, unique significant hypothetical parts of Artificial Intelligence in forming representative commitment likewise have been examined in this exploration work. The tone and heading of the exploration show that utilization of Artificial Intelligence decidedly affects the contemporary representative commitment exercises. An exploratory review configuration is created and considered for the conduction of this examination. A subjective philosophy has been viewed as in this examination relying upon the exploratory idea of the review to meet the exploration objective. A likelihood inspecting approach has been embraced by the scientist to choose a significant example size and gather expected information. Essential data collection process has been taken on in this review to upgrade legitimate examination result. The discoveries and conversations have demonstrated toward the end that Artificial Intelligence is significant for the contemporary associations to shape representative commitment to a positive way. © 2023 IEEE.

关键词： Artificial intelligence

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Deep-Learning-Based Intelligent PotholeEye+ Detection Pavement Distress Detection System 1

Deep-Learning-Based Intelligent PotholeEye+ Detection Paveme...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Gorintla, Shobana Kumar, B. Anil Chanadana, B. Sai Sai, N. Raghavendra Kumar, G. Sai Chaitanya Nri Institute of Technology Department of Computer Science and Engineering AP India Lingayas Institute of Management and Technology Department of Computer Science and Engineering India Vellore Institute of Technlogy VI-AP University School of Cse India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Dvr &hs Mic College of Engineering and Technology Department of Computer Science and Engineering AP India

ISBN: (纸本)9781665497107

Road maintenance is one of the most critical issues confronting agricultural based countries. Road foundations are vital to society since most traffic accidents are caused by poor road conditions such as potholes. Potholes are caused by poor quality and a failure to keep up with the roadways. Furthermore, the continuous rise of overweight vehicles, such as trucks, are responsible for the potholed roads. These poor quality roads will extremely damage the automobiles in terms of underinflated tires and accidents. Hence, a A suitable structure for monitoring road conditions and planning future works should be developed. Pits are generally formed by mature roadways and poor road support structures, and their number grows with time. Potholes compromise road safety and reduce transportation efficiency. This research study proposes a portable framework called PotholeEye + to consequently observe the outer layer of a road and progressively distinguish asphalt problems through video investigation. As you drive the road director, PotholeEye+ pre-processes the images, removes the inclusion and sort out the misery. This research work is testing PotholeEye+ on a real road constantly for a year by using original settings, a camera, a small-scale PC, a beneficiary GPS, etc. Subsequently, PotholeEye + has recognized asphalt problems with 92% typical accuracy, 87% accuracy and 74% review when driving at a typical speed of 110 km/h on an actual highway. © 2022 IEEE.

关键词： Deep learning

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Designing Scalable and Energy-Efficient Computation: Magnetoelectric Spin-Orbit Circuitry

Designing Scalable and Energy-Efficient Computation: Magneto...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Sharma, Amit Kumar Mehra, Ritika Kumar, Brijesh Balaji, N.V. Kumar, K. Sriram Gaikwad, V.S. Tmu Department of Physics India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand 248007 India School of Commerece and Management Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Information Technology Coimbatore641105 India Computer Engineering Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

Using a magnetoelectric spin-orbit circuit, this study suggests a revolutionary method for developing scale and cost-effective computer. Despite semiconductors miniaturization, the leading technology of today, CMOS, has basically not evolved in terms of operational ideas throughout the years. The research investigates alternate dimensions expanded logic strategies to boost the performance of numerical control schemes and allow for the growth of novel methods like machine learning. The proposed optical logic device links the spinning energy of the electron with its mobility through spin-orbit communication and magnetism property toggle. The gadget has collaboration and senses due to the incorporation of cutting-edge linked chemicals and quasi constituents. In the areas of shifting energy, voltage, and computation efficacy, the study shows that this novel technique beats the technology known as CMOS. Additionally, it allows for minimum idle power usage and the possibility of multigenerational computation. © 2023 IEEE.

关键词： Carrier transfer CMOS technology Electricity generation Energy usage Energy-efficient computation Ferroelectricity switching Interfacial and workload component control Logic complexity Logic efficiency Microprocessor innovation Scaling Semiconductor technology Spin-flux conversion Spin-orbit transmission Strain

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific Research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection 4

Enhanced Route Optimization: Incorporating Road Safety Facto...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Subash, R. Prathaban, Banu Priya Ganeshkumar, N. Gopinathan, S. Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai India Gitam School of Technology Gandhi Institute of Technology and Management Department of Computer Science and Engineering Karnataka Bengaluru562163 India Sri Sai Ram Institute of Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences. © 2024 IEEE.

关键词： Travel time

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Comparative Evaluation of Deep Learning Models in Alzheimer's Disease Diagnosis

Comparative Evaluation of Deep Learning Models in Alzheimer'...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Arya, Leena Sharma, Yogesh Kumar Nayak, Smitha Sivakumar, Janaki Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur522302 India Computing Department Muscat College Oman Programme Leader Computer Security and Forensics Department of Computer Science and Creative Technologies Global College of Engineering and Technology Muscat Oman

Alzheimer's Disease (AD) is an ever-evolving neurodegenerative confusion that influences the neurons of the mind in a manner that hinders the patients' ability to solve problems and remember issues and that requires timely and proper identification to enable appropriate management and treatment. Based on MRI medical images, deep learning models have become one of the most effective approaches for analyzing the signs of the early stages of AD. This research, therefore, carries out a comparative analysis of five deep learning models such as Convolutional Neural Networks (CNNs) like AlexNet, VGGNet, and ResNet, as well as Long Short-Term Memory (LSTM) networks and 3D Convolutional Neural Networks (3D-CNNs) in the finding of Alzheimer's Sickness from medical imaging data. Each model's arrangement parameters, for example, layer configurations, learning rate, batch size, optimizer, number of epochs, loss function, applying techniques for regularization, activation functions, and the input dimensions, using pre-trained weights, were carefully initialized to yield a proper comparison between the models. Diagnostic performances were evaluated utilizing precision, responsiveness(recall), specificity, and region under the receiver operating characteristic curve (AUC-ROC). ResNet outperformed other profound learning models in diagnosing Alzheimer's Disease from MRI scans with a precision of 0.91 and AUC-ROC of 0.95. At the same time, VGGNet, AlexNet, LSTM, and 3D-CNNs demonstrated varying strengths and limitations in balancing accuracy, computational efficiency, and feature extraction. © 2025 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Leveraging Digital technologies and System Dynamics for Achieving Sustainable Development Goals Across Diverse Sectors 4

Leveraging Digital Technologies and System Dynamics for Achi...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Duggal, Sonia Ather, Danish Yusupov, Ibrohimbek Sagar, Pinki Kumar, Raj Jain, Vishal Department of Computer Applications Faridabad India Amity University Department of It and Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan G.L. Bajaj Institute of Technology and Management Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

This paper aims at arguing the impact of digital technologies and system dynamics models towards the achievement of the United Nations - Sustainable Development Goals (UN-SDGs). Interrelated with numerous fields, this research focuses on the application on intelligent systems in big data environment and sensors networks to increase efficiency of resources, improve transparence of processes as well as to make sound decisions according to SDGs requirements. Based on findings of ten recent pieces of research, the paper integrates lessons and best practices with the aim of illustrating how technology can open new avenues for addressing complex problems. © 2024 IEEE.

关键词： Sustainable development goals

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Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains 4

Advancements in Deep Learning for Early Detection and Diagno...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Ather, Danish Yusupov, Ibrohimbek Duggal, Sonia Kumar, Raj Sagar, Pinki Jain, Vishal Amity University Department of It and Engineering Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Ibn Musa al-Khwarizm Department of Artificial Intelligence Tashkent Uzbekistan Department of Computer Applications Faridabad India G.L. Bajaj Institute of Technology and Management Greater Noida India Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350387490

Identification and diagnosis of diseases in its early stages is one of the biggest determinants of improving patient health and needs, as well as decreasing general health care costs. Hence, with the application of deep learning, there has been a total change in the methods of medical diagnosis. This scoping review provides an employment of the current research progress of deep learning in different disease types, such as cancer, neurodegenerative diseases, and ophthalmologic diseases. This paper intends to compare different deep learning architectures, preprocessing methodologies used for data, and how data is incorporated into the framework, with a view of giving a glimpse of what researchers are currently doing in this fast-growing field, difficulties that are being encountered, as well as the future direction of the research domain being considered in this paper. © 2024 IEEE.

关键词： Cancer Detection Convolutional Neural Networks Deep Learning Diagnosis Early Detection Machine Learning Medical Imaging Neurodegenerative Diseases Ophthalmology

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