Comparing text features involves evaluating the "similarity" between texts. It is crucial to use appropriate similarity measures when comparing similarities. This study utilized various techniques to assess the similarities between newspaper articles, including deep learning and a previously proposed method: a combination of Pointwise Mutual Information (PMI) and Word Pair Matching (WPM), denoted as PMI+WPM. For performance comparison, law data from medical research in Japan were utilized as validation data in evaluating the PMI+WPM method. The distribution of similarities in text data varies depending on the evaluation technique and genre, as revealed by the comparative analysis. For newspaper data, non-deep learning methods demonstrated better similarity evaluation accuracy than deep learning methods. Additionally, evaluating similarities in law data is more challenging than in newspaper articles. Despite deep learning being the prevalent method for evaluating textual similarities, this study demonstrates that non-deep learning methods can be effective regarding Japanese-based texts.

Communication with the deaf has always been crucial. Deaf and hard-of-hearing persons can now express their thoughts and opinions to teachers through sign language, which has become a universal language and a very effective tool. This helps to improve their education. This facilitates and simplifies the referral procedure between them and the teachers. There are various bodily movements used in sign language, including those of arms, legs, and face. Pure expressiveness, proximity, and shared interests are examples of nonverbal physical communication that is distinct from gestures that convey a particular message. The meanings of gestures vary depending on your social or cultural background and are quite unique. Sign language prediction recognition is a highly popular and Research is ongoing in this area, and the SVM has shown value. Research in a number of fields where SVMs struggle has encouraged the development of numerous applications, such as SVM for enormous data sets, SVM for multi-classification, and SVM for unbalanced data sets.Without a precise diagnosis of the signs, right control measures cannot be applied when they are needed. One of the methods that is frequently utilized for the identification and categorization of sign languages is image processing. African Buffalo Optimization using Support Vector Machine (ABO+SVM) classification technology is used in this work to help identify and categorize peoples' sign languages. Segmentation by K-means clustering is used to first identify the sign region, after which color and texture features are extracted. The accuracy, sensitivity, Precision, specificity, and F1-score of the proposed system African Buffalo Optimization using Support Vector Machine (ABOSVM) are validated against the existing classifiers SVM, CNN, and PSO+ANN.

Software cost and schedule estimation is usually based on the estimated size of the software. Advanced estimation techniques also make use of the diverse factors viz, nature of the project, staff skills available, time constraints, performance constraints, technology required and so on. Usually, estimation is based on an estimation model prepared with the help of experienced project managers. Estimation of software cost is predominantly a crucial activity as it incurs huge economic and strategic investment. However accurate estimation still remains a challenge as the algorithmic models used for Software Project planning and Estimation doesn't address the true dynamic nature of Software Development. This paper presents an efficient approach using the contemporary Constructive Cost Model (COCOMO) augmented with the desirable feature of fuzzy logic to address the uncertainty and flexibility associated with the cost drivers (Effort Multiplier Factor). The approach has been validated and interpreted by project experts and shows convincing results as compared to simple algorithmic models.

Mobile Ad hoc Network is a network of multiple wireless nodes which communicate and exchange information together without any fixed and centralized infrastructure. The core objective for the development of MANET is to provide movability, portability and extensibility. Due to infrastructure less network topology of the network changes frequently this causes many challenges for designing routing algorithms. Many routing protocols for MANET have been suggested for last few years and research is still going on. In this paper we review three main routing protocols namely Proactive, Reactive and Hybrid, performance comparison of Proactive such as DSDV, Reactive as AODV, DSR, TORA and Hybrid as ZRP in different network scenarios including dynamic network size, changing number of nodes, changing movability of nodes, in high movability and denser network and low movability and low traffic. This paper analyzes these scenarios on the performance evaluation metrics e.g. Throughput, Packet Delivery Ratio (PDR), Normalized Routing Load(NRL) and End To-End delay(ETE).This paper also reviews various network layer security attacks challenge by routing protocols, detection mechanism proposes to detect these attacks and compare performance of these attacks on evaluation metrics such as Routing Overhead, Transmission Delay and packet drop rates.

Introducing renewable energy sources, such as wind and photovoltaic arrays, in microgrids that supply remote regions with electricity represents a significant leap in electricity generation. Combining photovoltaic panels and diesel engines is one of the most common ways to supply electricity to rural communities. Such hybrid systems can reduce the cost of electricity generation in these remote power systems because they use free energy to balance the power generated by diesel engines. However, the combination of renewable energy sources and diesel engines tends to complicate the sizing and control of the entire system due to the intermittent nature of renewable energy sources. This study sought to investigate this issue in depth. It proposes a robust hybrid controller that can be used to facilitate optimum power sharing between a PV power source and diesel generators based on the dynamics of the available PV energy at any given time. The study also describes a hybrid PV-diesel power plant's essential functional parts that produce electricity for a microgrid using a renewable energy source. Power control needs to be adjusted to reduce the cost of power generation.

Existing PoS (Point of Sale) based payment frameworks are vulnerable as the Payment Application's integrity in the smart phone and PoS are compromised, vulnerable to reverse engineering attacks. In addition to these existing PoS (Point of Sale) based payment frameworks do not perform point-to-point encryption and do not ensure communication security. We propose a Smart and Secure PoS (SSPoS) Framework which overcomes these attacks. Our proposed SSPoS framework ensures point-to-point encryption (P2PE), Application hardening and Application wrapping. SSPoS framework overcomes repackaging attacks. SSPoS framework has very less communication and computation cost. SSPoS framework also addresses Heartbleed vulnerability. SSPoS protocol is successfully verified using Burrows-Abadi-Needham (BAN) logic, so it ensures all the security properties. SSPoS is threat modeled and implemented successfully.

This paper examines regularity and normality in soft separation axioms for soft bitopological ordered spaces and their relationships with other properties. The findings expand our understanding of bitopological ordered spaces. Previous research, such as Al-Shami's work [3], has established distinctions between separation axioms in topological ordered spaces, which are more effective in describing these spaces' properties.

Measurable electrophysiological changes in the scalp are frequently linked to brain activities. These progressions are called related evoked potentials (ERP), which are transient electrical responses recorded by electroencephalography (EEG) in light of tactile, mental, or motor enhancements. This painless strategy is gradually being used as a conclusion and clinical help. In this article, we will talk about the main ways to monitor brain activities in people with neurological diseases like Alzheimer's disease by analyzing EEG signals using ERP. We will also talk about how this method helps to detect the disease at an early stage.

The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

The Smart home energy consumption represents much of the total energy consumed in advanced countries. For this reason, the main objectif of this paper is to study the energy consumption profile by day for each home appliances: controllable appliances for example Washing machine, Tumble dryer and Air conditioning and uncontrollable appliances for example TV, PC, Lighting, Refrigerator and Electric heater. In this paper, we start with presentation of a smart home energy management systems. Next, we present the modeling and simulation of controllable appliances and uncontrollable appliances. Finally, concludes this paper with some prospects. The modeling and the simulation of a Smart home appliances is based on MATLAB/Simulink software.

In this paper we used classification algorithms on social networking. We are proposing, a new classification algorithm called the improved Decision Tree (IDT). Our model provides better classification accuracy than the existing systems for classifying the social network data. Here we examined the performance of some familiar classification algorithms regarding their accuracy with our proposed algorithm. We used Support Vector Machines, Naïve Bayes, k-Nearest Neighbors, decision tree in our research and performed analyses on social media dataset. Matlab is used for performing experiments. The result shows that the proposed algorithm achieves the best results with an accuracy of 84.66%.

Due to the increase in population and consequently the increase in the production of plastic waste, recovery of this part of the waste is an undeniable necessity. On the other hand, the recycling of plastic waste, if it is placed in a systematic process and controlled, can be effective in creating jobs and maintaining environmental health. Waste collection in many large cities has become a major problem due to lack of proper planning with increasing waste from population accumulation and changing consumption patterns. Today, waste management is no longer limited to waste collection, but waste collection is one of the important areas of its management, i.e. training, segregation, collection, recycling and processing. In this study, a systematic method based on machine vision for sorting plastic bottles in different colors for recycling purposes will be proposed. In this method, image classification and segmentation techniques were presented to improve the performance of plastic bottle classification. Evaluation of the proposed method and comparison with previous works showed the proper performance of this method.

The Internet of Things (IoT) is a novel concept that allows a large number of objects to be connected to the Internet while also allowing them to be controlled remotely. The Internet of Things is extensive and has become an almost inseparable part of our daily lives. Users' personal data is frequently obtained by these linked gadgets and stored online. In recent years, the security of acquired data has become a major concern. As devices grow more linked, privacy and security concerns grow more pressing, and they must be addressed as soon as possible. IoT implementations and devices are particularly vulnerable to attacks that might adversely affect customer security and privacy, which might have an impact on their practical utility. The goal of this study is to bring attention to the security and privacy concerns that exist in IoT systems. To that purpose, the paper examines security challenges at each level of the IoT protocol stack, identifies underlying impediments and critical security requirements, and provides a rapid overview of available security solutions for securing IoT in a layered environment.

The world today is advancing towards a digital solution for every indusial domain varying from advanced engineering and medicine to training and management. The supply cycles can only be boosted via an effective management of the warehouse and a stronger hold over the logistics and inventory insights. RFID technology has been an open source tool for various MNCs and corporal organization who have progressed along a considerable drift on the charts. RFID is a methodology of analysing the warehouse and logistic data and create useful information in line to the past trends and future forecasts. The method has a high tactical accuracy and has been seen providing up to 99.57% accurate insights for the future cycle, based on the organizational capabilities and available resources. This paper discusses the implementation of RFID on field and provides results of datasets retrieved from controlled data of a practical warehouse and logistics system.

Universities are open systems that aim to prepare students to meet academic and industrial programs' expectations. It is important for universities to recognize these expectations and to make sure that they are achievable. To do so, graduates' progression tracking system is an essential tool for universities' development to ensure graduate students meet the market requirements. The purpose of this paper is to create automatic tracing system that captures information about students after graduation and creates annual report that represents the status of university students in term of employment or completing their study. It mainly assists graduates to find appropriate jobs that meet their desires or enabling them to complete their higher education by providing all these opportunities in one platform. The system main objective is to improve communication between graduate students, the university and companies. It also aims to identify the difficulties associated with graduate employability and changes are required to serve current students in term of creating new programs or activities. This helps universities to identify and address the existing curriculums and program's strengths and weaknesses and their adequacy, quality and competencies of a graduate in the labor market, which enhances the quality of higher education. we analyzed and implemented the tracing system using PHP language, which speeds up custom web application development and MySQL database, which guarantee data security, high performance, and other features. Graduate students found the proposed system usable and valuable.

The outbreak of Covid-19 has forced teachers of special education in Saudi Arabia to keep to themselves to live in a technology-infused society throughout the virtual teaching and learning process. This study set out to explore the competence, self-efficacy, and autonomy in using information communication technology (ICT) of special education teachers in Saudi Arabia. A total of 244 special education teachers in Saudi Arabia participated in this study. This study adopted the New General Self-Efficacy Scale developed and validated by Chen, Gully, and Eden (2001), as well as the Basic Psychological Needs in Exercise Scale (BPNES) developed and validated by Vlachopoulos and Michailidou (2006). Confirmatory factor analysis (CFA) and multivariate analysis of variance (MANOVA) were used as the main data analysis in this study. The findings showed that special education teachers in Saudi Arabia possessed competence, self-efficacy, and autonomy in using ICT in their teaching and learning process. All the factor loadings in each factor were.75 or higher, indicating good factor loadings. The results of the MANOVA indicated that special education teachers in Saudi Arabia do not report different perceptions of their competence, self-efficacy, and autonomy despite their different gender, age group, academic background, and teaching experiences.

Cyber security plays an important role in the field of IT industry and other industry too. Whenever we talk about cyber security, the word cybercrime pops out. Cybercrime is the biggest issues we are facing right now. Every 39 seconds an attacker is hacking something. Since 2008 to 2019 there are more than 8800 data breach cases is being found or filed. Even as we are aware of cybercrime and its stats, only 5% organization are fully secured and other 95% are not fully secured. According to survey 56% organization have weak controls. Basically they are not secured. Apart from taking measures cyber security are facing huge challenges or disturbs to many. This paper mainly focuses on dare to cyber security and also center of attraction is cyber security expertise, morals with changing in technology with time. [1]

Information technology plays an important role in healthcare. The cloud has several applications in the fields of education, social media and medicine. But the advantage of the cloud for medical reasons is very appropriate, especially given the large volume of data generated by healthcare organizations. As in increasingly health organizations adopting towards electronic health records in the cloud which can be accessed around the world for various health issues regarding references, healthcare educational research and etc. Cloud computing has many advantages, such as "flexibility, cost and energy savings, resource sharing and rapid deployment". However, despite the significant benefits of using the cloud computing for health IT, data security, privacy, reliability, integration and portability are some of the main challenges and obstacles for its implementation. Health data are highly confidential records that should not be made available to unauthorized persons to protect the security of patient information. In this paper, we discuss the privacy and security requirement of EHS as well as privacy and security issues of EHS and also focus on a comprehensive review of the current and existing literature on Electronic health that uses a variety of approaches and procedures to handle security and privacy issues. The strengths and weaknesses of some of these methods were mentioned. The significance of security issues in the cloud computing environment is a challenge.

With the technological advances, the humans have made so much progress in the ease of living and now incorporating the use of sight, motion, sound, speech etc. for various application and software controls. In this paper, we have explored the project in which gestures plays a very significant role in the project. The topic of gesture control which has been researched a lot and is just getting evolved every day. We see the usage of computer vision in this project. The main objective that we achieved in this project is controlling the computer settings with hand gestures using computer vision. In this project we are creating a module which acts a volume controlling program in which we use hand gestures to control the computer system volume. We have included the use of OpenCV. This module is used in the implementation of hand gestures in computer controls. The module in execution uses the web camera of the computer to record the images or videos and then processes them to find the needed information and then based on the input, performs the action on the volume settings if that computer. The program has the functionality of increasing and decreasing the volume of the computer. The setup needed for the program execution is a web camera to record the input images and videos which will be given by the user. The program will perform gesture recognition with the help of OpenCV and python and its libraries and them it will recognize or identify the specified human gestures and use them to perform or carry out the changes in the device setting. The objective is to adjust the volume of a computer device without the need for physical interaction using a mouse or keyboard. OpenCV, a widely utilized tool for image processing and computer vision applications in this domain, enjoys extensive popularity. The OpenCV community consists of over 47,000 individuals, and as of a survey conducted in 2020, the estimated number of downloads exceeds 18 million.

Decentralized energy production without greenhouse gas emissions from renewable energy sources despite their advantage and environmental impact suffers from the problem of intermittent and fluctuating supply depending on weather conditions. To overcome this problem, energy storage is essential to enable reliable and continuous supply of the load. Hydrogen is one of the most promising energy storage solutions because it is easily transportable and can be used as fuel or as a raw material for the production of other chemicals.In this article, we will focus on hydrogen energy storage techniques using photovoltaic systems. We will review the different types of hydrogen storage structuresfor several applications, including residential and commercial buildings, as well as industry and transportation (electric vehicles using PEFMC fuel cells).

A common language for modelling software requirements and design in recent years is Unified Modeling Language (UML). Essential principles and rules are provided by UML to help visualize and comprehend complex software systems. It has therefore been incorporated into the curriculum for software engineering courses at several institutions all around the world. However, it is commonly recognized that UML is challenging for beginners to understand, mostly owing to its complexity and ill-defined nature. It is unavoidable that we need to comprehend their preferences and issues considerably better than we do presently in order to approach the problem of teaching UML to beginner students in an acceptable manner. This paper offers a hint based approach that can be implemented along with an ordinary lab task. Some keywords are heighted to indicate class diagram component and make students to understand the textual descriptions. The experimental results indicate significant improvement in students learning skills. Furthermore, majority of students also positively responded to the survey conducted in the end experimental study.

This study based on TAM and D&M model to examine the Kuwaiti employee performance using the electronic document and records management system (EDRMS) in the Ministry of social affairs and labor. Additionally, this study has proposed the moderating effect of work cooperation on employee performance Data of 345 employees were collected from Ministry of social affairs and labor in Kuwait. Smart PLS 3.0 was used to analyze the data. Results indicated that perceived ease of use and perceived usefulness have a positive influence on employee performance. However, findings do not support the relationship between system usage and user satisfaction. Additionally, the results show that there is a significant positive moderating effect of work cooperation. This research provides strong evidence for defining the key factors affecting system usage but also in view of its limits. It should be evaluated. Not all the factors affecting the intentions of end-users to use EDRMS have been fully covered. There are major variables, for example, facilitating state and perceived compatibility are important factors that can be covered in future research. This research is an addition to the current literature and the first attempt in this area to the best of authors' knowledge.

Laser induced breakdown spectroscopy (LIBS) technique has been used for the elemental composition of the soils. In this technique, a high energy laser pulse is focused on a sample to produce plasma. From the spectroscopic analysis of such plasma plume, we have determined the different elements present in the soil. This technique is effective and rapid for the qualitative and quantitative analysis of all type of samples. In this work a Q-switched Nd: YAG laser operating with its fundamental mode (1064 nm laser wavelength), 5 nanosecond pulse width, and 10 Hz repetition rate was focused on soil samples using 10 cm quartz lens. The emission spectra of soil consist of Iron (Fe), Calcium (Ca), Titanium (Ti), Silicon (Si), Aluminum (Al), Magnesium (Mg), Manganese (Mn), Potassium (K), Nickel (Ni), Chromium (Cr), Copper (Cu), Mercury (Hg), Barium (Ba), Vanadium (V), Lead (Pb), Nitrogen (N), Scandium (Sc), Hydrogen (H), Strontium (Sr), and Lithium (Li) with different finger-prints of the transition lines. The maximum intensity of the transition lines was observed close to the surface of the sample and it was decreased along the axial direction of the plasma expansion due to the thermalization and the recombination process. We have also determined the plasma parameters such as electron temperature and the electron number density of the plasma using Boltzmann's plot method as well as the Stark broadening of the transition lines respectively. The electron temperature is estimated at 14611 °K, whereas the electron number density i.e. 4.1 × 10¹⁶ cm^-3 lies close to the surface.

Cloud computing is now used by most companies, business centres and academic institutions to embrace new computer technology. Cloud Service Providers (CSPs) are limited to certain services, missing some of the assets requested by their customers, it means that different clouds need to interconnect to share resources and interoperate between them. The clouds may be interconnected in different characteristics and systems, and the network may be vulnerable to volatility or interference. While information technology and cloud computing are also advancing to accommodate the growing worldwide application, criminals use cyberspace to perform cybercrimes. Cloud services deployment is becoming highly prone to threats and intrusions. The unauthorised access or destruction of records yields significant catastrophic losses to organisations or agencies. Human intervention and Physical devices are not enough for protection and monitoring of cloud services; therefore, there is a need for more efficient design for cyber defence that is adaptable, flexible, robust and able to detect dangerous cybercrime such as a Denial of Service (DOS) and Distributed Denial of Service (DDOS) in heterogeneous cloud computing platforms and make essential real-time decisions for forensic investigation. This paper aims to develop a framework for digital forensic for the detection of cybercrime in a joined heterogeneous cloud setup. We developed a Digital Forensics model in this paper that can function in heterogeneous joint clouds. We used Unified Modeling Language (UML) specifically activity diagram in designing the proposed framework, then for deployment, we used an architectural modelling system in developing a framework. We developed an activity diagram that can accommodate the variability and complexities of the clouds when handling inter-cloud resources.

Objectives: To compare the levels of anxiety on GAD-7 scale among undergraduates of dental, medical and engineering students during COVID-19. The secondary objectives were to correlate the factors influencing level of anxiety and to assess the coping strategies practiced by undergraduates' students of Karachi duri.ng COVID-19 outbreak. Methodology: The cross-sectional based survey was conducted online among the medical, dental and engineering undergraduates' university students of private sector in Karachi through purposive sampling technique during COVID-19 lock down period. The GAD-7 validated tool was used along with the demographic variables, related stress factors and the coping skills practiced during this outbreak. Total 571 questionnaires were found completed in all sections. The data was analyzed on SPSS version 23. P-value <0.05 was considered as statistically significant. Results: The mental health of the students was assessed on GADS-7 scale as normal, mild, moderate and severe levels. From the total (n=18-3.2%) were normal, (n=132-23.1%) had mild, (n=343-60.1%) had moderate and (n=78-13.7%) had severe anxiety level on GADS-7. The levels of anxiety on GAD-7 scale were all positively associated with the related stressors at p-value of 0.000. Moreover the results depicted that there was a moderate and positive correlation found (0.456, 0.447, 0.512 and 0.452) for all related stressors and GAD-7 scale. Taking breaks from watching, reading news regarding the outbreak of COVID-19, meditation and engaging in some other activities were the most frequently used coping strategies for all levels of anxiety among three cohorts of undergraduates'. Conclusion: Undergraduates has shown 96.9% drastically increased level of anxiety during the outbreak of COVID 19 pandemic. Taking breaks from watching, reading news regarding the outbreak of COVID-19 was the most frequent behavior practiced by the students.