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Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach
Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE. -
An Interrogation of Android Application-Based Privilege Escalation Attacks
Android is among the most widely used operating systems among consumers. The standard security model must address several dangers while still being usable by non-security users due to the wide range of use cases, including access to cameras and microphones and use cases for sharing information, entertainment, business, and health. The Android operating system has taken smartphone technology to peoples front doors. Thanks to recent technological developments, people from all walks of life can now access it. However, the popularity of the Android platform has exacerbated the growth of cybercrime via mobile devices. The open-source nature of its operating system has made it a target for hackers. This research paper examines the comparative study of the Android Security domain in-depth, classifying the attacks on the Android device. The study covers various threats and security measures linked to these kinds and thoroughly examines the fundamental problems in the Android security field. This work compares and contrasts several malware detection techniques regarding their methods and constraints. Researchers will utilize the information to comprehensively understand Android security from various perspectives, enabling them to develop a more complete, trustworthy, and beneficial response to Androids vulnerabilities. 2023 American Institute of Physics Inc.. All rights reserved. -
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE. -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
Influence of nano ?-Al2O3 as sintering aid on the microstructure of spray dried and sintered ?-Al2O3 ceramics
Alpha Alumina (?-Al2O3) has traditionally been sintered to near theoretical density by employing variations in raw material properties, particle sizes, grinding methods, compaction pressures, sintering aids or minor quantities of additives and sintering temperatures. All these parameters directly influence the grain growth morphology and microstructure of the sintered alumina ceramic characteristics. Growth of large grained microstructure facilitated by fine grinding of raw material and coalescence of the grains enhanced by dopant additions are well researched. The maximum sintered density and strength of the fired body could be attained through large grained microstructure which include near spheroidal grains. Most of the final sintering is accomplished via additions of suitable aids which also may be promoted by liquid-phase sintering which is considered highly advantageous compared to solid-state sintering for products in many defense applications. In this paper the influence of nano ?-Al2O3 (<100 nm particle size) as sintering aid to obtain the desired microstructure in sintered micron sized (1 to 5 m) ?-Al2O3 is being reported. 1.0 and 1.5 wt% nano ?- Al2O3 powder were spray dried with 99.0 and 98.5% ?-Al2O3 powder respectively, with polyvinyl alcohol binder, compacted into 10 mm dia and 5 mm thick pellets and sintered at 1450 C with 3 h soak time. In addition to the two different sintering aid additive percentages, other variables included are spray dried powders removed from (i) chamber and (ii) cyclone. The sintered ceramics were characterized for bulk density and fracture surface microstructure via SEM analysis. Nano alumina as sintering aid exhibited significant influence that included generation of microstructure with porosity, precipitation or liquid phase sintering. The study was limited to establishing the definitive role played by nano alumina to influence the sintering of micron alumina. 2022 -
Synthesis of high temperature (1150 C) resistant materials after extraction of oxides of Al and Mg from Aluminum dross
Aluminum Dross (Al-dross) is a well-known Industrial waste generated in an Aluminium industry from the melting of the metal itself. It gets made yearly in hundreds of thousands of tons worldwide, due to the wide use and demand of Aluminum in almost every industry. However, Al-dross is not completely a waste as it contains two compounds of interest, namely Aluminum Oxide (Al2O3) and Magnesium Aluminate (MgAl2O4). They are the basic compounds present in any refractory which are products featuring low thermal conductivity and high temperature shock characteristics in the order of 1000 C+. Thus, Aluminum Dross becomes a vital candidate to be considered for the extraction of the two of the aforementioned compounds. Recent studies have shown that Al-dross indeed can be used to extract Al2O3 and MgAl2O4. However, Al-dross also contains Aluminum Nitride (AlN) a compound that exhibits the exact opposite properties demonstrated by refractories. In addition to being technically unsuitable for use as refractory material, AlN also possesses another huge issue. When Al-dross is dumped into landfills, the AlN present in the dross combines with the moisture in the soil and is energized by geothermal heat which leads into an exothermic reaction, thereby releases highly toxic and health hazardous gases. Keeping the above techno-environment challenges in mind, prior to utilizing the beneficiated Al-dross in any industrial application, it is important to leach out the AlN from the dross in an environment friendly manner. This paper deals with the successive leaching of AlN from the Al-dross using two laboratory procedures. Sintered (to be added) pellets made out of the processed powder in the lab were subjected to analysis of structural phases and chemical constituents by employing XRD and EDS. Cyclic thermal shock test cycles were also carried out by subjecting the pellets to 1150 C and quenching in air alternately, to study the refractory characteristics. 2019 Elsevier Ltd. All rights reserved. -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved. -
Synthesis of Value Added Refractories from Aluminium Dross and Zirconia Composites
Results of a developmental study on the potential to synthesize industrial grade refractories from aluminum dross with un-stabilized zirconia are reported. The merit of the developed product to perform as refractories suitable for use at or above 1000C was assessed by studying the thermo-physical behavior as per guidelines of ASTM and IS. Aluminum dross, an industrial waste (slag) is generated in several millions of tons in the production of Aluminum and is dumped into landfills, which releases poisonous gases like methane and ammonia upon contact with moisture present in the land and the heat generated by the earth, warranting stringent mitigation efforts. Rich in aluminum metal (?15%), ?-Al2O3 (7-15%), MgAl2O4 (10-15%) and AlN (20-30%), the general prime dross composition draws interest due to its abundance and presence of ?-Al2O3 and MgAl2O4, for the production of refractories with insulating, shock resistance and stability at high temperature (?1000C and above) characteristics. Nevertheless, presence of AlN, a good thermal conductor acts as a deterrent in the production of refractories. Aluminium dross (after leaching out AlN) was processed with un-stabilized zirconia (monoclinic ZrO2) to synthesize the refractory composites. Conventional process (calcination, ball milling, compaction and sintering (1550C/6 hrs)) was employed. Characterization involved thermal shock cycling (air quench at furnace ambient of 1000C and room temperature) to determine the number of shock cycles endured before failure. Structural phase analyses at various stages of processing were carried out by via XRD. Magnesia present in dross did not appear to stabilize either the tetragonal or cubic ZrO2. Microstructural and chemical composition studies were carried out via SEM and EDS. The favourable results confirm the viability of the process methodology. 2019 Elsevier Ltd. -
Plasma Sprayed Refractory Coatings from Aluminium Dross
Refractory coatings on metals offer a unique blend of chemical inertness, stability and mechanical properties at temperatures higher than what the metal can normally withstand. However, a balance must be struck with many factors: Thickness, adhesion, performance, durability, economy and suitability for specific end use requirements. The present-day technology requires the coating to give effective service under extreme temperatures while being environmentally friendly and be easily available. One application of refractory coating is in steel industry-pipe linings. This research works highlights the potential to use aluminum dross, an industrial waste material to generate refractory coatings, comprised of Al2O3 and MgAl2O4 after suitable processing. Al dross is a byproduct of the Aluminium smelting process which can be recycled mechanically to separate the residual Aluminium metal from the Aluminium oxide. These are usually produced in tones every year and are found to be dumped in landfills and other empty spaces which generate toxic fumes like methane and other gases when reacted with moisture. The Aluminium dross used in this work was analyzed and found to comprise of its usual constituents such as metallic Al, MgAl2O4, Al2O3, AlN and other oxides and nitrides in minute quantities. Manual procedures were conducted to synthesize plasma spray-able dross which was further introduced to standard laboratory tests for the removal of undesirable constituents like AlN and other nitrides which led to the optimization of quality of powders. Atmospheric plasma spray (APS) coating methodology was used to deposit 250?m thick coatings of re-processed Al dross, involving the spraying of the processed powder onto a bond coated (NiCrAlY) steel substrate. The raw, reprocessed and the plasma sprayed coated Al dross were evaluated for their material characteristics by employing X-ray Diffractometry (XRD) for crystal structural phases, microstructure and chemical composition by employing sophisticated microscopy (SEM) technique and EDS associated with the SEM. The paper is presented keeping in in view the aptness of reprocessed Al dross, an industrial waste material to be utilized as refractories for use in engineering industries. 2019 Elsevier Ltd. -
Residual stress analysis on functionally graded 8% Y2O3-ZrO2 and NiCrAlY thermal barrier coatings
Thermal Barrier Coatings (TBCs) protect metallic components that operate in high temperature environments and enhance their service life. The conventional two-layered TBC system consists of a duplex ceramic top coat (TC) fabricated from 8 wt% yttria stabilized zirconia (8-YSZ) and an underlying bond coat (BC) comprised of intermetallic layers such as NiAl or MCrAlY (M = Co, Ni) etc. In the present study, functionally graded material (FGM) TBCs were fabricated by introducing a third blend layer of 8-YSZ and NiCrAlY, in between the BC and TC in order to enhance the thermal fatigue life of the TBC. The blend layer in FGM TBCs provides a smoother transition in thermal expansion properties between the metallic substrate and the top ceramic coat (8YSZ) which have widely different thermal expansion characteristics compared with each other. In service, thermal fatigue introduces severe tensile stresses between the coated layers and the substrates leading to ultimate detachment of the coatings from the substrates. In this work, residual stress analysis by Cos ? method was carried out as a non-destructive assessment tool to foresee the likelihood of onset of failure in the TBCs, well before the damage was visible. The two-layered (conventional) and three-layered (FGM) TBCs were synthesized on Inconel 718 substrates by atmospheric plasma spray (APS) technique. The TBCs were subjected to thermal fatigue tests between 1200? (by using gas flame) and ambient temperature and evaluated for residual stress analysis at different stages of thermal fatigue testing. The goal was to assess if residual stress analysis could be used to determine if the TBC was about to fail well before the delamination occurred and the catastrophic failure could be avoided. The tests conducted and results obtained are presented. 2022 -
Efficient Lung Cancer Classification on Multi level Convolution Neural Network using Histopathological Images
Lung cancer can be detected by lung nodules, which are a key sign. An early diagnosis enhances the likelihood that the patient will survive by enabling the appropriate therapy to start. To reduce the responsibility of radiologists' difficult and time-consuming labour of finding and categorising malignancy in Computed Tomography (CT) images, researchers have created CAD (computer-assisted diagnosis) systems. The likelihood and kind of malignancy are commonly determined by pathologists using histopathological images of biopsy specimens taken from potentially sick areas of the lungs. To categorise lung nodule malignancy, we recommend employing a four-level convolutional neural network (ML-CNN). From lung nodule CT scan images, multiple scales are extracted. ML-CNN's employs four CNNs network model structure. After the result of the last pooling layer has been flattened to a vector with a single dimension for each level, the vectors are concatenated. These four ML-CNNs will help our model perform better. The ML-CNN model can recognise and classify different forms of lung cancer with reasonable accuracy. The 25000 images employed in the ML-CNN model have been separated into three categories: training, validation, and testing. Three distinct tissue types were assessed and training and validation took up within 80% and 15% of the total time and 5% for testing, respectively. The histopathological images included the following tissue type's 1.Benign tissue 2. Large cell carcinoma 3.squamous cell carcinoma. The proposed model demonstrated superior performance on both the training set, achieving an accuracy of 78%, and the validation set, achieving an accuracy of 89.6% by the end of the evaluation 2023 IEEE. -
Improvement to Recommendation system using Hybrid techniques
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE. -
Influence of heat treatment on the tensile and hardness characteristics of friction stir weld joints of dissimilar aluminium alloys
Friction stir welding (FSW) is a solid-state low energy input welding technique. Most capable of joining very high strength alloys, which are finding wide range of applications in automobile and aerospace components. The current research focuses on the influence of post weld heat treatment on mechanical properties of friction stir weld joints of AA 7075 and AA 5052 dissimilar aluminum alloys. The trial experiments have been carried out using design of experiments (L16 Orthogonal Array) and the optimized process parameters have been selected based on the maximum hardness and the corresponding ultimate tensile strength (UTS). Further, the friction stir welding is accomplished with optimized process parameters (L9 Experimental trial) viz., the feed rate of 100?mm/min, tool rotational speed of 1200?rpm, tool offset of (-) 0.5?mm and using a cylindrical taper pin tool profile. The post heat treatment has been carried out on the friction stir weld joints obtained using the optimized parameters and the mechanical properties of the L9 Heat Treated (L9 - HT) and L9 - Non Heat Treated (L9 - NHT) specimens have been compared. The results shows that the post heat treated weld joints have higher micro hardness and tensile strength compared to the non-heat-treated weld joints. This is majorly attributed to recrystallization and elimination of voids due to the change in the microstructure of the weld joint. 2022 Author(s). -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 The Author(s). -
Bipolar Disease Data Prediction Using Adaptive Structure Convolutional Neuron Classifier Using Deep Learning
The symptoms of bipolar disorder include extreme mood swings. It is the most common mental health disorder and is often overlooked in all age groups. Bipolar disorder is often inherited, but not all siblings in a family will have bipolar disorder. In recent years, bipolar disorder has been characterised by unsatisfactory clinical diagnosis and treatment. Relapse rates and misdiagnosis are persistent problems with the disease. Bipolar disorder has yet to be precisely determined. To overcome this issue, the proposed work Adaptive Structure Convolutional Neuron Classifier (ASCNC) method to identify bipolar disorder. The Imbalanced Subclass Feature Filtering (ISF2) for visualising bipolar data was originally intended to extract and communicate meaningful information from complex bipolar datasets in order to predict and improve day-to-day analytics. Using the Scaled Features Chi-square Testing (SFCsT), extract the maximum dimensional features in the bipolar dataset and assign weights. In order to select features that have the largest Chi-square score, the Chi-square value for each feature should be calculated between it and the target. Before extracting features for the training and testing method, evaluate the Softmax neural activation function to compute the average weight of the features before the feature weights. Diagnostic criteria for bipolar disorder are discussed as an assessment strategy that helps diagnose the disorder. It then discusses appropriate treatments for children and their families. Finally, it presents some conclusions about managing people with bipolar disorder. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Review on EMG-based Pattern Identification Methods for Effective Controlling of Hand Prostheses
The ability of amputees to do daily duties is significantly restricted by upper limb amputation. The myoelectric prosthesis uses impulses from the surviving muscles in the stump to gradually restore function to such severed limbs. Such myosignals are unfortunately tedious and challenging to gather and employ. The process of transforming it into a user control signal after it has been acquired often consumes a significant amount of processing resources. By modifying machine learning strategies for pattern recognition, the factors that influence the traditional electromyography (EMG)-pattern identification approaches may be significantly minimized. Although more recent developments in intelligent pattern recognition algorithms could discern between a variety of degrees of freedom with high levels of accuracy, their usefulness in practical (amputee) applications was less obvious. This review paper examined how well various pattern recognition algorithms for hand prostheses performed. Finally, we discussed the current difficulties and offered some suggestions for future research in our paper's conclusion. 2023 IEEE. -
Unmanned Artificial Intelligence-Based Financial Volatility Prediction in International Stock Market
This study investigates the capacity of autonomous artificial intelligence to predict the volatility of the worldwide stock market and proposes an innovative approach utilizing cutting-edge AI algorithms. A comprehensive literature review examines the evolution of financial prediction systems and the transformative effects of artificial intelligence in improving predictive capabilities. The AI system under consideration employs machine learning techniques more effectively than traditional methods for collecting and predicting financial volatility. The strategy heavily relies on automated data capture, preprocessing, and model training. A recall of 76%, an accuracy rate of 94%, a precision of 81%, an area under the curve of 0.87, and a sharp ratio of 1.25 comprise the model's impressive specifications. This research illuminates the prospective financial applications of artificial intelligence and provides a way to navigate the intricacies of international stock markets. 2024 IEEE. -
Are Women Employees in Engineering Institutions Suffer from the Stress: An Investigation Approach
Workplace stress could be detrimental to both the employer and its employees. The greatest ways to keep distress at bay in the workplace are via competent management and well-organized processes. Supervisors must recognize signs of staff distress and be prepared to provide assistance. Whenever an individual is confronted with job expectations and pressures exceeding their skillset and expertise, they experience stress connected to their employment. Workplace stress is common and could exacerbate stress when workers don't perceive they have the backing of management or coworkers in dealing with the challenges they face. The term 'stressed' is frequently used as a justification for ineffective management and inadequate supervision, even though it is typically caused by a misunderstanding of the difference between pressure and challenge. Stress and other forms of adversity are at an all-time high, both in the job and in personal life. Employee stress could be further exacerbated by things like job uncertainty, excessive hours, frequent changes, workload, and unattainable targets. The purpose of this article is to understand the variables that contribute to high-stress levels amongst female faculty members in engineering institutions strain in the job atmosphere. One's ability to manage stressful work situations, the amount of social help and assistance one receives, and the coping mechanisms one employs all play significant roles in how much stress one endures on the job. This research was done because it was necessary and important. In addition, women play a larger role in society than males do. This study suggests that women experience much high levels of stress than men do employment reasons, repercussions, roles and obligations of women faculty in engineering education, and possible remedies are all explored in this paper. 2023 IEEE. -
KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE.