A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

Vacancy defect MoSeTe embedded in N and F co-doped carbon skeleton for high performance sodium ion batteries and hybrid capacitors

Sodium-ion batteries(SIBs) and hybrid capacitors(SIHCs) have garnered significant attention in energy storage due to their inherent advantages,including high energy density,cost-effectiveness,and enhanced safety.However,developing high-performance anode materials to improve sodium storage performa nce still remains a major challenge.Here,a facile one-pot method has been developed to fabricate a hybrid of MoSeTe nanosheets implanted within the N,F co-doped honeycomb carbon skeleton(MoSeTe/N,F@C).Experimental results demonstrate that the incorporation of large-sized Te atoms into MoSeTe nanosheets enlarges the layer spacing and creates abundant anion vacancies,which effectively facilitate the insertion/extraction of Na^(+) and provide numerous ion adsorption sites for rapid surface capacitive behavior.Additionally,the heteroatoms N,F co-doped honeycomb carbon skeleton with a highly conductive network can restrain the volume expansion and boost reaction kinetics within the electrode.As anticipated,the MoSeTe/N,F@C anode exhibits high reversible capacities along with exceptional cycle stability.When coupled with Na_(3)V_(2)(PO_(4))_(3)@C(NVPF@C) to form SIB full cells,the anode delivers a reversible specific capacity of 126 mA h g^(-1) after 100 cycles at 0.1 A g^(-1).Furthermore,when combined with AC to form SIHC full cells,the anode demonstrates excellent cycling stability with a reversible specific capacity of50 mA h g^(-1) keeping over 3700 cycles at 1.0 A g^(-1).In situ XRD,ex situ TEM characterization,and theoretical calculations(DFT) further confirm the reversibility of sodium storage in MoSeTe/N,F@C anode materials during electrochemical reactions,highlighting their potential for widespread practical application.This work provides new insights into the promising utilization of advanced transition metal dichalcogenides as anode materials for Na^(+)-based energy storage devices.

Bioconvective Hybrid Flow with Microorganisms Migration and Buongiorno’s Model under Convective Condition

Heat transfer improves significantly when the working fluid has high thermal conductivity.Heat transfer can be found in fields such as food processing,solar through collectors,and drug delivery.Considering this notable fact,this work is focused on investigating the bio-convection-enhanced heat transfer in the existence of convective boundary conditions in the flow of hybrid nanofluid across a stretching surface.Buongiorno fluid model with hybrid nanoparticles has been employed along the swimming microorganisms to investigate the mixture base working fluid.The developed nonlinear flow governing equations have been tackled numerically with the help of the bvp4c.The effects of relevant parameters on the flowdynamic have been portrayed in a graphical representation.The velocity profile decreases by raising the levels of buoyancy ratio and mixed convection in the range of 0.1<λ≤0.3.It has been discovered thatwhen bioconvection levels rise,motile microbemigration abruptly slows,which results in a decrease in fluid acceleration.The concentration of fluid flow declined for the Lewis number,but the opposite trend has been observed for the elastic parameter,thermophoresis parameter,and buoyancy ratio.With rising values of Brownian motion and thermophoretic diffusion,the surface drag and Nusselt number decrease significantly.Whereas,the opposite trend has been observed when the values of the thermal Biot number,Prandtl number and buoyancy ratio are enhanced.Additionally,data from this study have been validated by comparison with those that have previously been published,and an appropriate rate of agreement has been observed.

Fireworks Optimization with Deep Learning-Based Arabic Handwritten Characters Recognition Model

Handwritten character recognition becomes one of the challenging research matters.More studies were presented for recognizing letters of various languages.The availability of Arabic handwritten characters databases was confined.Almost a quarter of a billion people worldwide write and speak Arabic.More historical books and files indicate a vital data set for many Arab nationswritten in Arabic.Recently,Arabic handwritten character recognition(AHCR)has grabbed the attention and has become a difficult topic for pattern recognition and computer vision(CV).Therefore,this study develops fireworks optimizationwith the deep learning-based AHCR(FWODL-AHCR)technique.Themajor intention of the FWODL-AHCR technique is to recognize the distinct handwritten characters in the Arabic language.It initially pre-processes the handwritten images to improve their quality of them.Then,the RetinaNet-based deep convolutional neural network is applied as a feature extractor to produce feature vectors.Next,the deep echo state network(DESN)model is utilized to classify handwritten characters.Finally,the FWO algorithm is exploited as a hyperparameter tuning strategy to boost recognition performance.Various simulations in series were performed to exhibit the enhanced performance of the FWODL-AHCR technique.The comparison study portrayed the supremacy of the FWODL-AHCR technique over other approaches,with 99.91%and 98.94%on Hijja and AHCD datasets,respectively.

Chaotic Elephant Herd Optimization with Machine Learning for Arabic Hate Speech Detection

In recent years,the usage of social networking sites has considerably increased in the Arab world.It has empowered individuals to express their opinions,especially in politics.Furthermore,various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales.This is attributed to business owners’understanding of social media’s importance for business development.However,the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns.Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies.In this background,the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection(CEHOML-HSD)model in the context of the Arabic language.The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal.To attain this,the CEHOML-HSD model follows different sub-processes as discussed herewith.At the initial stage,the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer.Secondly,the Support Vector Machine(SVM)model is utilized to detect and classify the hate speech texts made in the Arabic language.Lastly,the CEHO approach is employed for fine-tuning the parameters involved in SVM.This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm.The design of the CEHO algorithm for parameter tuning shows the novelty of the work.A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach.The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches.

室内装修用特制定向刨花板

本文介绍了一种加拿大产特制定向刨花板,该板材是为室内装修和家具制造而特制的,通过对其生产原材料、生产工艺特点、产品性能的分析,并就该产品在室内装修领域进行了探讨,期望室内设计师、家具设计师和用户对该特制定向刨花板有全新的认识。

BERT-CNN: A Deep Learning Model for Detecting Emotions from Text

Due to the widespread usage of social media in our recent daily lifestyles,sentiment analysis becomes an important field in pattern recognition and Natural Language Processing(NLP).In this field,users’feedback data on a specific issue are evaluated and analyzed.Detecting emotions within the text is therefore considered one of the important challenges of the current NLP research.Emotions have been widely studied in psychology and behavioral science as they are an integral part of the human nature.Emotions describe a state of mind of distinct behaviors,feelings,thoughts and experiences.The main objective of this paper is to propose a new model named BERT-CNN to detect emotions from text.This model is formed by a combination of the Bidirectional Encoder Representations from Transformer(BERT)and the Convolutional Neural networks(CNN)for textual classification.This model embraces the BERT to train the word semantic representation language model.According to the word context,the semantic vector is dynamically generated and then placed into the CNN to predict the output.Results of a comparative study proved that the BERT-CNN model overcomes the state-of-art baseline performance produced by different models in the literature using the semeval 2019 task3 dataset and ISEAR datasets.The BERTCNN model achieves an accuracy of 94.7%and an F1-score of 94%for semeval2019 task3 dataset and an accuracy of 75.8%and an F1-score of 76%for ISEAR dataset.

Study on Individual Distance of the Non-Japanese for the Small Mobile Robot

This study seeks to establish techniques of architecture planning for living robots and human together. This experiment is elemental step to search relations between a small mobile robot and human. This experiment is ＂How can a small mobile robot approach to individuals distances of foreigners （non-Japanese） up to the point where foreigners （non-Japanese） do not feel like approaching any more＂. It focuses on a small mobile robot and identifies distances at which the robot can approach to individuals, particularly adult foreign male. This paper clarifies how the individual distances change by the robot＇s speed and angle of approach varies.

Personal Space of Small Mobile Robot Moving towards Standing or Sitting Elderly Individuals

Recently, Japan has experienced a low birthrate and an aging population. The development of communication robots, such as cleaning and a care-giver robot, has been progressing. Care-giver robots provide daily assistance, including contacting emergency services. This study is part of the ＂study on planning techniques of living space in harmony with robots＂, and focused on the elderly. Minimum distance was the subjects felt ＂I do not want any more approached＂. Subjects were 21 elderly persons （eight males and thirteen females）, aged 66-86 years. The experimental room was an assembly room in a public accommodation （14 m× 6.5 m）. The small mobile robot used in this experiment was external form dimensions of 120 mm （W）× 130 mm （D）× 70 mm （H）, In this experiment, considering the personal space as the small mobile robot is watching robot without support function for person. The robot moved toward standing or sitting subjects at constant velocities from a distance 5 m apart. Research factors are 5 angles （0°, 45°, 90°, 135° and 180°） and 2 speeds （0.08 m/s and 0.24 m/s）.

Architectural Planning Living with Robotics

This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The concept of the intelligent space emerged from the field of robotics in the mid-1990s. The idea of the intelligent space is that the robot will support the architecture and users of the space by keeping in close contact with architectural space equipped with intelligent technologies. The benefit of the intelligent space is that the robot can be smaller and less intelligent because the robot will be assisted by advanced technologies embedded in the architectural space, such as sensors and actuators. Therefore, the robot can consequently assist user＇s activities with delicate care. This paper describes the vision and possibility for architectural planning as it relates to live with robots who can support the inhabitants＇ lives. This paper introduces the study of the relationship between humans and moving robot in architectural space, especially support region for humans by desktop mobile robot.

A Study on Feelings of Worriment Due to Middle-Sized Robot in Living Space

Recently, the demand of services for architectural space is more increasing due to the diversification and complication of people＇s needs. In this research, it is assumed that the robot will support people＇s various needs and people＇s lives in the near future. In this study, the authors investigated the relationship between a robot and a person in quasi space close to actual life space. The authors carried out an experiment to clarify the feelings of worriment due to a robot in the living room in the case of Japanese males. This research aims to establish architecture techniques for robots and humans living together. In this experiment, the robot approached a male sitting on a sofa in quasi space. At different distances between the robot and the male, the subject evaluated his feelings of worriment from ＂no worriment＂ to ＂worriment＂ in five steps from one to five. The robot used in this experiment is middle-sized （380 mm （L） × 380 mm （W） × 350 mm （H））. In this experiment, it is considered that the robot asks for help outside when an emergency occurs. In this experiment, the authors had three situations for the subject： watching television, reading a magazine and just sitting.

Hunter Prey Optimization with Hybrid Deep Learning for Fake News Detection on Arabic Corpus

Nowadays,the usage of socialmedia platforms is rapidly increasing,and rumours or false information are also rising,especially among Arab nations.This false information is harmful to society and individuals.Blocking and detecting the spread of fake news in Arabic becomes critical.Several artificial intelligence(AI)methods,including contemporary transformer techniques,BERT,were used to detect fake news.Thus,fake news in Arabic is identified by utilizing AI approaches.This article develops a new hunterprey optimization with hybrid deep learning-based fake news detection(HPOHDL-FND)model on the Arabic corpus.The HPOHDL-FND technique undergoes extensive data pre-processing steps to transform the input data into a useful format.Besides,the HPOHDL-FND technique utilizes long-term memory with a recurrent neural network(LSTM-RNN)model for fake news detection and classification.Finally,hunter prey optimization(HPO)algorithm is exploited for optimal modification of the hyperparameters related to the LSTM-RNN model.The performance validation of the HPOHDL-FND technique is tested using two Arabic datasets.The outcomes exemplified better performance over the other existing techniques with maximum accuracy of 96.57%and 93.53%on Covid19Fakes and satirical datasets,respectively.

Understanding of Ultrasonic Assisted Machining with Diamond Grinding Tool

In this work, machining test was carried out in various machining conditions using ultrasonic vibration capable CNC machine. For work material, alumina ceramic (Al2O3) was used while for tool material diamond electroplated grinding wheel was used. To evaluate ultrasonic vibration effect, grinding test was performed with and without ultrasonic vibration in same machining condition. In ultrasonic mode, ultrasonic vibration of 20 kHz was generated by HSK 63 ultrasonic actuator. On the other hand, grinding forces were measured by KISTLER dynamometer. And an optimal sampling rate for grinding force measurement was obtained by a signal processing and frequency analysis. The surface roughness of the ceramic was also measured by using stylus type surface roughness instrument and atomic force microscope (AFM). Besides, the scanning electron microscope (SEM) was used for observation of surface integrality.

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language,categorized by its rich and complicated grammatical formats.Furthermore,the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns.The Arabic language consists of distinct variations utilized in a community and particular situations.Social media sites are a medium for expressing opinions and social phenomena like racism,hatred,offensive language,and all kinds of verbal violence.Such conduct does not impact particular nations,communities,or groups only,extending beyond such areas into people’s everyday lives.This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection(IALODL-OHSD)on Arabic Cross-Corpora.The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media.In the IALODL-OHSD model,a threestage process is performed,namely pre-processing,word embedding,and classification.Primarily,data pre-processing is performed to transform the Arabic social media text into a useful format.In addition,the word2vec word embedding process is utilized to produce word embeddings.The attentionbased cascaded long short-term memory(ACLSTM)model is utilized for the classification process.Finally,the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results.To illustrate a brief result analysis of the IALODL-OHSD model,a detailed set of simulations were performed.The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.

Hyperparameter Tuned Deep Learning Enabled Intrusion Detection on Internet of Everything Environment

Internet of Everything(IoE),the recent technological advancement,represents an interconnected network of people,processes,data,and things.In recent times,IoE gained significant attention among entrepreneurs,individuals,and communities owing to its realization of intense values from the connected entities.On the other hand,the massive increase in data generation from IoE applications enables the transmission of big data,from contextawaremachines,into useful data.Security and privacy pose serious challenges in designing IoE environment which can be addressed by developing effective Intrusion Detection Systems(IDS).In this background,the current study develops Intelligent Multiverse Optimization with Deep Learning Enabled Intrusion Detection System(IMVO-DLIDS)for IoT environment.The presented IMVO-DLIDS model focuses on identification and classification of intrusions in IoT environment.The proposed IMVO-DLIDS model follows a three-stage process.At first,data pre-processing is performed to convert the actual data into useful format.In addition,Chaotic Local Search Whale Optimization Algorithm-based Feature Selection(CLSWOA-FS)technique is employed to choose the optimal feature subsets.Finally,MVO algorithm is exploited with Bidirectional Gated Recurrent Unit(BiGRU)model for classification.Here,the novelty of the work is the application of MVO algorithm in fine-turning the hyperparameters involved in BiGRU model.The experimental validation was conducted for the proposed IMVO-DLIDS model on benchmark datasets and the results were assessed under distinct measures.An extensive comparative study was conducted and the results confirmed the promising outcomes of IMVO-DLIDS approach compared to other approaches.

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

Deep Transfer Learning-Enabled Activity Identification and Fall Detection for Disabled People

The human motion data collected using wearables like smartwatches can be used for activity recognition and emergency event detection.This is especially applicable in the case of elderly or disabled people who live self-reliantly in their homes.These sensors produce a huge volume of physical activity data that necessitates real-time recognition,especially during emergencies.Falling is one of the most important problems confronted by older people and people with movement disabilities.Numerous previous techniques were introduced and a few used webcam to monitor the activity of elderly or disabled people.But,the costs incurred upon installation and operation are high,whereas the technology is relevant only for indoor environments.Currently,commercial wearables use a wireless emergency transmitter that produces a number of false alarms and restricts a user’s movements.Against this background,the current study develops an Improved WhaleOptimizationwithDeep Learning-Enabled Fall Detection for Disabled People(IWODL-FDDP)model.The presented IWODL-FDDP model aims to identify the fall events to assist disabled people.The presented IWODLFDDP model applies an image filtering approach to pre-process the image.Besides,the EfficientNet-B0 model is utilized to generate valuable feature vector sets.Next,the Bidirectional Long Short Term Memory(BiLSTM)model is used for the recognition and classification of fall events.Finally,the IWO method is leveraged to fine-tune the hyperparameters related to the BiLSTM method,which shows the novelty of the work.The experimental analysis outcomes established the superior performance of the proposed IWODL-FDDP method with a maximum accuracy of 97.02%.

Coati Optimization-Based Energy Efficient Routing Protocol for Unmanned Aerial Vehicle Communication

With the flexible deployment and high mobility of Unmanned Aerial Vehicles(UAVs)in an open environment,they have generated con-siderable attention in military and civil applications intending to enable ubiquitous connectivity and foster agile communications.The difficulty stems from features other than mobile ad-hoc network(MANET),namely aerial mobility in three-dimensional space and often changing topology.In the UAV network,a single node serves as a forwarding,transmitting,and receiving node at the same time.Typically,the communication path is multi-hop,and routing significantly affects the network’s performance.A lot of effort should be invested in performance analysis for selecting the optimum routing system.With this motivation,this study modelled a new Coati Optimization Algorithm-based Energy-Efficient Routing Process for Unmanned Aerial Vehicle Communication(COAER-UAVC)technique.The presented COAER-UAVC technique establishes effective routes for communication between the UAVs.It is primarily based on the coati characteristics in nature:if attacking and hunting iguanas and escaping from predators.Besides,the presented COAER-UAVC technique concentrates on the design of fitness functions to minimize energy utilization and communication delay.A varied group of simulations was performed to depict the optimum performance of the COAER-UAVC system.The experimental results verified that the COAER-UAVC technique had assured improved performance over other approaches.