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1st International Conference on Artificial Intelligence and Data Analytics, CAIDA 2021

作者： Waheed, shaikh Abdul Revathi, s. Matheen, Mohammed Abdul Khan Lodhi, Amairullah Ashrafuddin, Mohammed Maboobatcha, G.s. B.S. Abdur Rahman Crescent Institute of Science Technology Dept. of Computer Science and Engineering Chennai India King Saud University Common First Year Dept. of SDS Riyadh Saudi Arabia Shadan College of Engineering and Technology Dept. of Electronics and Communication Engineering Hyderabad India King Saud University Common First Year Dept. of SDS Riyadh Saudi Arabia

ISBN: (纸本)9780738131771

Emotions are different biological states brought on by neurophysiological changes associated with the nervous system which are affected and results in thoughts, feelings, behavioral responses, and a degree of pleasure or displeasure. These emotions play a vital role in understanding the human response towards any means of action they experience. The system uses an EEG headband device made out of IoT and various machine learning algorithms to understand the human's emotion of any incident they undergo. In this paper, we propose a method to detect and recognize the emotional changes in a human who is exposed to various images. © 2021 IEEE.

关键词： Cost effectiveness

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Expression of Concern for: Analysis of Network Technologies and Cyber security Assessment for Enhancing Machine Learning, Grid Computing and Cyber-Physical Connectivity Internetwork Effectiveness

Expression of Concern for: Analysis of Network Technologies ...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： b. Dhanalakshmi Chaitanya singh s. surya Atharav Hedage shikha Kuchhal Korakod Tongkachok Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamilnadu India Computer Engineering Department Swarrnim Startup & Innovation University Gandhinagar Gujarat India Computer Science and Engineering School of Computing Kalasalingam University Sirivelliputhur Tamilnadu India Electronics and Telecommunications Vishwakarma Institute of Information Technology Pune Belgaum Karnataka India Department of ECE SPITM DCRUST Murthal Faculty of Law Thaksin University Thailand

ISBN: (数字)9781665466431

ISBN: (纸本)9781665466448

The fast improvement of the Internet has incredibly worked with individuals spreading information worldwide, prompting the Internet turning into a pivotal apparatus in many fields like science, trade, schooling, etc. With the Internet common in such countless parts of day to day existence, network security has become b.s.c and requests consistent consideration. For this situation, an IDs's motivation is to recognize between real organization associations and possibly hurtful ones. The recommended method in this examination centers around an interruption identification and avoidance system that comprises of Cloudlet Controller (CC), Trust Authority (TA), and Virtual Machine Manager to recognize questionable practices in a cloud climate. Cloud clients are migrated to different regions in the climate, as indicated by our proposed approach. Whenever information bundles from different clients are gathered, network traffic happens. To resolve this issue, we assembled the Cloudlet Controller, which gets bundles from different clients through switch. CC gets parcels until they arrive at their edge level; if the cloudlet is over-burden, the bundles are shipped off inactive cloudlets. In this philosophy, network traffic is decreased, and it is significantly helpful in identifying interruptions in a straightforward way. In this review, a cross breed arrangement b.s.d interruption location and counteraction framework on the cloud is worked to distinguish gatecrasher exercises in the framework. A cross breed characterization b.s.d interruption location framework is created with a few cloudlets, a cloudlet regulator, a confided in power, and a virtual machine supervisor to distinguish interloper assaults in the framework, as well as a deterrent instrument to safeguard bundles from gatecrashers.

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IoT b.s.d smart Cradle for Neonatal Monitoring

IoT based Smart Cradle for Neonatal Monitoring

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： R. sonia sujay Mugaloremutt Jayadeva s D Prabu Ragavendiran Revathi N Jeevanantham Arumugam s sandeep Kumar Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu India Department of Health System Management Studies JSS Academy of Higher Education & Research Mysuru Karnataka India Department of Computer Science and Engineering RVS Technical campus Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Panimalar Institute of Technology Chennai Tamil Nadu India Department of Information Technology Kongu Engineering College Perundurai Tamil Nadu India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

ISBN: (纸本)9781665482721

India has the highest percentage of working women and mothers. It also has a record for the highest number of babies being born every single day. These two statements highly contradict each other. When a woman gives birth to a baby, she needs to spend almost a year or two taking care of the necessities of the baby. but when a need to leave the baby for a certain time arises regularly, it becomes tougher for them to do so. This study provides a solution to this problem. This research aims at the construction of a smart cradle. This cradle is des igned using various input elements, output elements, a controller module, and a mobile application. The input elements consist of a temperature sensor, a sound sensor or a microphone,a moisture sensor, and a music player. The output elements include the motor and The controller used in this study is the NodeMCU module. Cloud storage is also used and it is provided by the Arduino loT software. The sensors in the smart cradle begin recording relevant parameters such as temperature, humidity, and sound as soon as it is turned on. The information is then uploaded to the cloud The data is then processed in the cloud for analysis. If indeed the child's temperatures is over the safe range, the user receives a warning. Moisture levels above normal suggest that the infant has urinated The sound of the baby crying will be recorded in the microphone. The output elements are energized as a result of the sensor's data. If the baby has a high fever or has to go to the bathroom, the app will notify the parent.

关键词： Temperature sensors Pediatrics Cloud computing Temperature distribution Moisture Humidity software

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A hybrid mental health prediction model using support Vector Machine, Multilayer Perceptron, and Random Forest algorithms

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Healthcare Analytics 2023年 3卷 100185-100185页

作者： Mohamed, E. syed Naqishbandi, Tawseef Ahmad bukhari, syed Ahmad Chan Rauf, Insha sawrikar, Vilas Hussain, Arshad Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai 600048 India Division of Computer Science Mathematics and Science Collins College of Professional Studies St. John's University NY United States Department of Psychiatry Government Medical College Srinagar Jammu and Kashmir 190010 India Department of Clinical and Health Psychology University of Edinburgh Edinburgh United Kingdom

The prevalence and burden of mental health disorders are on the rise in conflict zones, and if left untreated, they can lead to considerable lifetime disability. Following the repeal of Article 370, political unrest spread quickly, forcing the Indian government to impose safety precautions such as lockdowns and communication ban. Consequently, the region of Kashmir experienced a marked rise in anxiety as a result of these lifestyle changes. Machine learning has proven useful in the early diagnosis and prognosis of certain diseases. Therefore, this study aims to classify anxiety problems early by utilising a pre-clinical mental health dataset collected after the abrogation of article 370 in Kashmir. The first part of the paper aims at developing and implementing a prediction model b.s.d on classification into one of the five pre-clinical anxiety stages, i.e., stage 1: minimal anxiety, stage 2: mild anxiety, stage 3: moderate anxiety, stage 4: severe anxiety, and stage 5: very severe anxiety. The second part offers recommendations for those suffering from anxiety disorders. Feature selection and prediction are used to predict the correct stage of anxiety for b.s. possible medical intervention. Three different algorithms: support Vector Machine(sVM), Multilayer Perceptron (MLP), and Random Forest (RF), are employed for predicting anxiety stages. Among them, random forest (RF) achieved 98.13% accuracy. A forecasted likelihood condition was assessed to provide a suitable recommendation. Further, accuracy and kappa statistics are used to assess the performance of the suggested model, which offers a significant addition to predicting anxiety early, and exhibits high prediction and recommendation accuracy. This study aims to assist mental health professionals and experts in making quick and accurate choices. © 2023 The Author(s)

关键词： Machine learning Mental health Multilayer Perceptron Prediction Random Forest support Vector Machine

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Explainable deep stacking ensemble model for accurate and transparent brain tumor diagnosis

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computers in biology and Medicine 2025年 191卷 110166-110166页

作者： Haque, Rezaul Khan, Mahbub Alam Rahman, Hamdadur Khan, shakil siddiqui, Md Ismail Hossain Limon, Zishad Hossain swapno, s M Masfequier Rahman Appaji, Abhishek Department of Computer Science and Engineering East West University A 2 Jahurul Islam Ave Dhaka1212 Bangladesh Department of Management Information System Pacific State University 3424 Wilshire Blvd. 12th Floor Los Angeles CA90010 United States Department of Management of Information System International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Business Analytics International American University 3440 Wilshire Blvd STE 1000 Los AngelesCA90010 United States Department of Engineering/Industrial Management Westcliff University IrvineCA92614 United States Department of Computer Science Westcliff University IrvineCA92614 United States Department Of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur-2 Dhaka1216 Bangladesh Department of Medical Electronics Engineering B.M.S. College of Engineering Bangalore560019 India Maastricht University University Eye Clinic Maastricht Maastricht Netherlands

Early detection of brain tumors in MRI images is vital for improving treatment results. However, deep learning models face challenges like limited dataset diversity, class imbalance, and insufficient interpretability. Most studies rely on small, single-source datasets and do not combine different feature extraction techniques for better classification. To address these challenges, we propose a rob.s. and explainable stacking ensemble model for multiclass brain tumor classification. To address these challenges, we propose a stacking ensemble model that combines EfficientNetb0, MobileNetV2, GoogleNet, and Multi-level CapsuleNet, using Catboost as the meta-learner for improved feature aggregation and classification accuracy. This ensemble approach captures complex tumor characteristics while enhancing rob.s.ness and interpretability. The proposed model integrates EfficientNetb0, MobileNetV2, GoogleNet, and a Multi-level CapsuleNet within a stacking framework, utilizing Catboost as the meta-learner to improve feature aggregation and classification accuracy. We created two large MRI datasets by merging data from four sources: braTs, Msoud, br35H, and sARTAJ. To tackle class imbalance, we applied borderline-sMOTE and data augmentation. We also utilized feature extraction methods, along with PCA and Gray Wolf Optimization (GWO). Our model was validated through confidence interval analysis and statistical tests, demonstrating superior performance. Error analysis revealed misclassification trends, and we assessed computational efficiency regarding inference speed and resource usage. The proposed ensemble achieved 97.81% F1 score and 98.75% PR AUC on M1, and 98.32% F1 score with 99.34% PR AUC on M2. Moreover, the model consistently surpassed state-of-the-art CNNs, Vision Transformers, and other ensemble methods in classifying brain tumors across individual four datasets. Finally, we developed a web-b.s.d diagnostic tool that enables clinicians to interact with the proposed mo

关键词： Magnetic resonance imaging

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Two New Kinds of Interference Alignment schemes for Cellular K-user MIMO Downlink Networks

arXiv

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arXiv 2021年

作者： Li, Jingfu Feng, Wenjiang Yu, F. Richard Jiang, Weiheng The School of Microelectronics and Communication Engineering Chongqing University Chongqing400044 China The Department of System and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

It is known that interference alignment (IA) plays an important role in improving the degree of freedom (DoF) of multi-input and multi-output (MIMO) systems. However, most of the traditional IA schemes suffer from the high computational complexity and require the global and instantaneous channel state information (CsI), both of which make them difficult to be extended to cellular MIMO systems. To handle these issues, two new interference alignment schemes, i.e., the retrospective interference regeneration (RIR) scheme and the beamforming b.s.d distributed retrospective interference alignment (b-DRIA) scheme, are proposed for cellular K-user MIMO downlink networks. For the RIR scheme, it adopts interference elimination algorithm to erase redundant symbols in inter-cell interference (ICI) signals, and then uses interference regeneration algorithm to avoid secondary interference. The RIR scheme obtains greater DoF gain than the retrospective interference alignment (RIA) scheme, but incurs performance degradation when the transceiver antennas ratio (TAR) approaches 1. Therefore, the b-DRIA scheme is further proposed. For the b-DRIA scheme, the cellular beamforming matrix is introduced to eliminate the ICI, and meanwhile distributed retrospective interference alignment algorithm is adopted to align inter-user interference (IUI). The simulation results show that the b-DRIA scheme obtains larger DoF than the RIR scheme locally. specifically, when TAR approaches 1, two schemes obtain the same DoF. While TAR approaches 2, the DoF of the b-DRIA scheme is superior than the RIR scheme. © 2021, CC bY.

关键词： beamforming

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Jseun: Joint semantic-Instance segmentation of Point Cloud Via Neighbor Features for Indoor scenes

SSRN

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ssRN 2023年

作者： Chen, Hui Jiang, Jinlong Chen, Wanlou Liu, Wanquan Liu, Peter Xiaoping College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Nanovision Medical Technology Co. Ltd Shanghai200000 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada

Efficient and accurate segmentation of the point cloud for indoor scenes is an important task for 3D scene understanding. Due to the complexity of indoor environments. It is difficult to fully extract the details of point cloud, resulting in poor recognizability in existing models. In this paper, a novel framework called joint semantic-instance segmentation via enhancedutilization of neighbor features is proposed to achieve semantic and instance segmentation of indoor scenes. This framework mines point cloud information from a local scale, but it is different from other models in that it focuses on the full mining of point cloud neighbor information. In this regard, a new neighbor feature extraction module is developed to enrich the expression of neighborhood information and enlarge the construction of the neighborhood neighbor scope. b.s.des, semantic and instance segmentations take advantages of each other by constructing a mutual promotion model of semantic and instance, which can obtain more accurate semantic and instance predictions for indoor scenes. The proposed scheme is evaluated on the s3DIs dataset and compared with existing methods. Experimental results demonstrate that this method is more effective and efficient than previous approaches in joint 3D semantic and instance segmentation. © 2023, The Authors. All rights reserved.

关键词： semantics

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An Implementation of Energy Demand Forecast using J48 and simple K Means 5

An Implementation of Energy Demand Forecast using J48 and Si...

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5th IEEE International Conference on science, Technology, engineering and Mathematics, ICONsTEM 2019

作者： Dhanalakshmi, J. Ayyanathan, N. B.S. Abdur Rahman Crescent Institute of Science and Technology Deemed to Be University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781728115993

Energy utilization has exponentially expanded all around over the previous decade. Energy the board is vital to future financial success and ecological security. Energy is related with mechanical creation, agrarian yield, wellbeing, water get to, populace, instruction, personal satisfaction, and so on. The board of Energy request is required to appropriately allot accessible assets. A few new strategies for Energy request the board are being utilized over the previous decade to anticipate the future Energy needs precisely. J48 and simple K Means are Energy request estimating methods that are being received. For as long as ten years, the dataset has been produced from California's yearly power request. Year has been considered as info parameters for bio-Gas, bio-Mass and solar Thermal where the interest for burden in the J48 and simple K Means framework is the single yield. The extent of uses and strategies guaging is very substantial, and this article centers around the techniques used to foresee Energy utilization. © 2019 IEEE.

关键词： Energy utilization

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Forecasting water level of Glacial fed perennial river using a genetically optimized hybrid Machine learning model

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Materials Today: Proceedings 2021年 46卷 11113-11119页

作者： Mirza Imran P. sheikh Abdul Khader Mohammd Rafiq Kishan singh Rawat Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science And Technology Vandalur Ch 41 India National Atmospheric Research Laboratory Department of Space Government of India Gadanki 517112 India Geo-Informatics Civil Engineering Department Graphic Era (Deemed to be University) Dehradun 248002 Uttrakhand India

The floods are the significant damaging disasters in the world. Either the causes of floods can be natural or anthropogenic but in both cases people have to bear the burden of devastation. The forecasting of water levels of a flood prone river is must in order to minimize the life and property damages. In this paper, we propose a hybrid machine learning model of Adaptive Neuro Fuzzy Inference system (ANFIs) and Genetic algorithm, which is trained on the temperature, precipitation data of two Meteorological stations and water level of the river. Different ANFIs models with different membership functions and optimization techniques are tested and the b.s. performing model is picked for further modification. The parameters of selected ANFIs model are then optimized using the genetic algorithm to get better results. The ANFIs optimized with genetic algorithm showed better results as compared to the conventional ANFIs.

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synergistic Impact of Magnesium Compound as a Potential Dye Additive for Organic-b.s.d sensitizer in Dsscs

SSRN

引用

ssRN 2022年

作者： Khan, Azlina Ahmad Abdullah, M.H. Rahman, M.A. Faizal Yusof, M. syarifah Adilah Mamat, Mohamad Hafiz bin Radzol, Afaf Md sin, N. Diyana sabani, N. Hayati banu, I.b. shameen Rusop, M. School of Electrical Engineering College of Engineering Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Permatang Pauh13500 Malaysia College of Electrical Engineering Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Permatang Pauh13500 Malaysia Universiti Malaysia Perlis Perlis Kangar01000 Malaysia Department of Physics B.S. Abdur Rahman Crescent Institute of Science & Technology Vandalur Chennai600 048 India

In this study, the dye-sensitized solar cell (DssCs) was successfully fabricated using a dye molecule sensitizer extracted from Mitragyna speciosa (Ms), while MgsO 4 salt compound served as a potent dye additive for dye treatment. The optimal amount of MgsO 4 additive that contributed to the highest power conversion efficiency, PCE was investigated. Field emission sEM, FEsEM, X-ray diffraction, XRD, Fourier transform-IR, FTIR, UV-Vis spectroscopy, current to voltage characteristics, I-V and incidence photon to current efficiency, IPCE were used to investigate the structural, optical, chemical and also the electrical properties of the DssC cells and their components. It was found that the extracted Ms dye provided evidence of the presence of chlorophyll pigment, an effective light-harvesting pigment necessary for the production of charge transfer and electricity. Also, MgsO 4 dye additive-b.s.d DssCs outperformed the untreated dye cell. At their optimal dye additive of 0.3 g (6 ml of chlorophyll dye solution), the current density, Jsc and the associated PCE of the DssC was higher at 2.08 mA/cm 2 and 0.39%, respectively. Thus, using Ms as a dye sensitizer with optimized MgsO 4 compound as the additive showed better photovoltaic effects, increased solar light absorption and enhanced photon energy utilisation. © 2022, The Authors. All rights reserved.

关键词： Magnesium compounds

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