Computer Science Applications articles list

WhatsApp is mobile application which allows exchange of messages, videos, audio’s and images via Smartphone. The increased use of IM on phones has turned to be goldmine for mobile and computer age. This paper focuses on understanding experimental viewpoint about the intensity, usage of whatsapp messenger and its impact on the academic performance of students in institutions. Instead of fast communication and enhancing effective flow of information and idea sharing among students, whatsapp has actually impacted in some pessimistic performance of students. For instance it takes up much of the students study time resulting in procrastination related issues , destroys students’ linguistic skills, leads to lack of concentration during lectures, results in difficulty in balancing online activities (whatsapp) and intellectual preparation and distracts learner from completing their assignments and adhering to their private studies time table. It is very userfriendly and easy to get initiated. Simply enter the telephone number of the device into the app. It then sorts through the contacts (with your permission) on the phone to figure out who else also has the app already installed. Users can then invite more contacts or go ahead and start sending messages to the ones that the app discovered. Brian Acton and Jan Koum (2009) invented Whatsapp messenger for easy and fast communication and distribution of multimedia messaging. Whatsapp is one of the trend and fashion in technology that is commonly used on specific mobile phones and computers. Since the Smartphones became popular, many messaging services were launched but Whatsapp has become widespread among them. The service is available free for one year and later user has to pay very less annual amount. Besides all, this Application is highly fanatic and can create a great impact on regular users, and apart from that it can leave a trace that becomes difficult to control and cure. With whatsapp messenger, communication through mobile phones has become easier, faster and cheaper. It is less expensive as compared to the normal phone messaging. An individual can chat with friends and family overseas through whatsapp without having to incur global SMS charges.

Sunita Singh

In the research described in this paper, we used computer simulations to analyze and compare different types of game strategies in the popular board game Don't Get Angry. Following a brief introduction, we summarized a few previous research papers examining similar board games' game strategies. Next, after a review of the Don't Get Angry game's official rules, we outlined four strategies that can be applied to increase the likelihood of winning. We simulated 50,000 games in which all four players made their moves randomly and 50,000 games where each used a different strategy. We tracked how frequently each player finished first, second, third, or last during the simulations. Furthermore, we recorded how many rounds were needed to complete the game for each player, how many times the players’ pawns were kicked out and returned to their houses by other players, and the number of players’ remaining steps during every gameplay. From the analysis of the recorded data, we could conclude that significant differences exist in the chances of winning the game for the examined strategies when all players use different strategies. The results improve the specific domain knowledge for the Don't Get Angry board game. It may help create more vigorous computer opponents and encourage further study to create a tool for evaluating students' strategic thinking while playing.

Ladislav Végh

Radio resource allocation in VCN is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user has to miss their important work. In this paper, we propose radio resource allocation scheme so that user can utilize their time by taking the advantage of subscription plan. In this scenario, multitype vehicle identification scheme from real time traffic database is proposed, its history will match in transport database and vehicle travelling history database. Proposed method indicates 95% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.

Vartika agarwal

Researchers have developed several authentication techniques for route predictions based on user requirements. These techniques estimate the shortest path and available resources in vehicular communication networks. In the current research, the existing authentication techniques for vehicular communication are compared and their inadequacies are identified. Then, new authentication technique based on route forecasting are presented for vehicular communication networks, with the service provider anticipating alternate routes for customers if the current routes have more network traffic congestion. By presenting the most efficient route, the suggested model allows users to maximise their time efficiency. Using A* algorithm, VCN agent seeks path with less network traffic congestion. This algorithm determines the shortest path between a source and a destination. Users are provided with several options by the service provider. User accepts the finest option that meets their needs. This method allows the service provider to deliver at least 15 routes within three seconds. This strategy is beneficial when a significant number of vehicles are stuck in traffic and consumers require network resources to utilise their time effectively.

Vartika agarwal

Various types of security attacks are normal in vehicular communication networks. The current study uses a support vector machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique for Vehicular Communication Network (VCN) resource management. Greatest quality of P2PCPM is that it eliminates attacked nodes from the network for the smooth process of vehicular communication. This scheme works well in terms of accuracy as well as attack detection rate. The whole simulation is made and tried by utilizing MATLAB Software. Simulation result shows 99% accuracy in case of security attack detection as well as reduced training and testing error upto 2%. Experimental results indicate that this scheme has a great efficiency and works well up to 1000 nodes, which is the limitation of current implementation. In future, simulation test may be done for unlimited nodes using similar or other techniques of attack detection.

Vartika agarwal

Internet of things describes the network of physical objects such as sensors, receivers, transmitters and other technologies which are used in VCN. In Vehicular communication network two or more vehicles are communicate with each other. VCN use advanced technologies to solve transportation related problems like long traffic delays, road accidents and air pollution. IOT based technologies make vehicular network smart. In this chapter we reviewed about network resource allocation security techniques, challenges and also discuss how we can make vehicular communication network smarter. We reviewed about different models and schemes for V2V communication. These schemes were developed to ensure a fair, efficient and transparent allocation of resource in an intelligent transportation system.

Vartika agarwal

Vehicle-to-vehicle (V2V) communication is an advance application and thrust area of research. In the current research, the authors highlighted the technologies which are used in V2V communication systems. Advantage of such technology is that it helps to detect live location and tolling. It plays an important role if there are huge amount of traffic. The current research work can obtain more information about Li-Fi, RFID, VANET, and LORAWAN technology. Li-Fi is known as VLC communication system that uses visible light for high data transmission and reception. RFID technology helps the emergency vehicle to reach destination quickly by avoiding any kind of traffic. LORAWAN is a large-scale network technology with a long range and VANET with low power that allows to obtain accurate traffic information on each route and this saves time. The comparison between the different technologies is reviewed in order to obtain the optimized technology as per the applications.

Vartika agarwal

This paper investigates the deep learning techniques to improve radio resource management (RRM) in vehicular communication network (VCN). In this paper, the deep learning algorithms are highlighted which are used for RRM. Deep learning technique in RRM is basically used to train the model using various algorithms of resource management including network data. Various machine learning tools will be helpful to get best solutions for resource allocation in a large cellular network.

Vartika agarwal

Currently, vehicle to vehicle communication is an important application and thrust area of research. In this paper the author highlighted the workings, executions, implementations and the application of the Internet of Things (IoT) in transport management and vehicle to vehicle communication systems. The main advantage of this Industry 4.0 based IoT technology is that it helps us to reduce road traffic and accidents. The limitations of GPS like accuracy, precision, effective analysis, etc. has led to the evolution of Mobile based V2V communication which is more effective, error proof, result oriented and smart. For proper analysis of traffic vehicle to vehicle communication is established. Random Data from vehicles taken by numerous sensors. Any car coming in its variety could effortlessly share the data by either of two cars nearby. With the help of vehicle to vehicle communication we can provide a path of emergency vehicles to reach the destination quickly. Based on the V2V application, Red and Green signals can be marked on the path as per traffic density and the emergency vehicle can take the shortest, fastest and low density route. Similar such examples are elaborated in the current research manuscript that will help the researcher in effectively finding the research gap for further advancement, analysis, innovation and optimization.

Vartika agarwal

Scheduling is the need of every project. Without scheduling, projects don’t fulfill time constraints, go over spending plan. It is a process utilized by teams to organize and structure their resources so the tasks they need to complete are scheduled based on availability and capability. This process is more beneficial if it is required to allocate and assign task to the resources without allocating their schedules. This paper reviews different types of network scheduling techniques, which are used to schedule a task. These techniques are very effective and help us to preplan the whole scheduling process

Vartika agarwal

The domestication of animals and cultivation of crops have been essential to human development throughout history, with the agricultural sector playing a pivotal role. Insufficient nutrition often leads to plant diseases, such as those affecting rice crops, resulting in yield losses of 20-40% of total production. These losses carry significant global economic consequences. Timely disease diagnosis is critical for implementing effective treatments and mitigating financial losses. However, despite technological advancements, rice disease diagnosis primarily depends on manual methods. In this study, we present a novel Self-Attention Network (SANET) based on the ResNet50 architecture, incorporating a kernel attention mechanism for accurate AI-assisted rice disease classification. We employ attention modules to extract contextual dependencies within images, focusing on essential features for disease identification. Using a publicly available rice disease dataset comprising four classes (three disease types and healthy leaves), we conducted cross-validated classification experiments to evaluate our proposed model. The results reveal that the attention-based mechanism effectively guides the Convolutional Neural Network (CNN) in learning valuable features, resulting in accurate image classification and reduced performance variation compared to state-of-the-art methods. Our SANET model achieved a test set accuracy of 98.71%, surpassing current leading models. These findings highlight the potential for widespread AI adoption in agricultural disease diagnosis and management, ultimately enhancing efficiency and effectiveness within the sector.

Mehdhar S. A. M. Al-Gaashani

Tomato is one of the most important vegetables worldwide. It is considered a mainstayof many countries’ economies. However, tomato crops are vulnerable to many diseasesthat lead to reducing or destroying production, and for this reason, early and accuratediagnosis of tomato diseases is very urgent. For this reason, many deep learning modelshave been developed to automate tomato leaf disease classification. Deep learning isfar superior to traditional machine learning with loads of data, but traditional machinelearning may outperform deep learning for limited training data. The authors proposea tomato leaf disease classification method by exploiting transfer learning and featuresconcatenation. The authors extract features using pre-trained kernels (weights) fromMobileNetV2 and NASNetMobile; then, they concatenate and reduce the dimensionalityof these features using kernel principal component analysis. Following that, they feedthese features into a conventional learning algorithm. The experimental results confirmthe effectiveness of concatenated features for boosting the performance of classifiers.The authors have evaluated the three most popular traditional machine learning classifiers,random forest, support vector machine, and multinomial logistic regression; amongthem, multinomial logistic regression achieved the best performance with an averageaccuracy of 97%.

Mehdhar S. A. M. Al-Gaashani

Colon cancer is the second most common cause of cancer death in women and the third most common cause of cancer death in men. Therefore, early detection of this cancer can lead to lower infection and death rates. In this research, we propose a new lightweight deep learning approach based on a Convolutional Neural Network (CNN) for efficient colon cancer detection. In our method, the input histopathological images are normalized before feeding them into our CNN model, and then colon cancer detection is performed. The efficiency of the proposed system is analyzed with publicly available histopathological images database and compared with the state-of-the-art existing methods for colon cancer detection. The result analysis demonstrates that the proposed deep model for colon cancer detection provides a higher accuracy of 99.50%, which is considered the best accuracy compared with the majority of other deep learning approaches. Because of this high result, the proposed approach is computationally efficient.

Mehdhar S. A. M. Al-Gaashani

The quality of software systems may be seriously impacted by specific types of source code anomalies. For example, poor programming practices result in Code Smells (CSs), which are a specific type of source code anomalies. They lead to architectural problems that consequently impact some significant software quality attributes, such as maintainability, portability, and reuse. To reduce the risk of introducing CSs and alleviate their consequences, the knowledge and skills of developers and architects is essential. On the other hand, ontologies, which are an artificial intelligence technique, have been used as a solution to deal with different software engineering challenges. Hence, the aim of this paper is to describe an ontological approach to representing and analyzing code smells. Since ontologies are a formal language based on description logics, this approach may contribute to formally analyzing the information about code smells, for example, to detect inconsistencies or infer new knowledge with the support of a reasoner. In addition, this proposal may support the training of software developers by providing the most relevant information on code smells. This ontology can also be a means of representing the knowledge on CSs from different sources (documents in natural language, relational databases, HTML documents, etc.). Therefore, it could be a valuable knowledge base to support the struggle of software developers and architects either to avoid CSs or to detect and remove them. The ontology was developed following a sound methodology. The well-known tool Protégé was used to manage the ontology and it was validated by using different techniques. An experiment was conducted to demonstrate the applicability of the ontology and evaluate its impact on speeding up the analysis of CSs.

Mehdhar S. A. M. Al-Gaashani

Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).

Mehdhar S. A. M. Al-Gaashani

Nuclei segmentation plays an essential role in histology analysis. The nuclei segmentation in histology images is challenging in variable conditions (clinical wild), such as poor staining quality, stain variability, tissue variability, and conditions having higher morphological variability. Recently, some deep learning models have been proposed for nuclei segmentation. However, these models rarely solve the problems mentioned above simultaneously. Most of the information in Hematoxylin and Eosin (H&E) stained histology images is in its channel, and the remaining information is in the spatial domain. We observed that most problems could be solved by considering channel and spatial features simultaneously, e.g., the spatial and channel features provide the solution to the morphological variability and staining variability, respectively. Therefore, we propose a novel spatial-channel attention-based modified UNet architecture with ResNet blocks in encoder layers. The UNet baseline preserves coarse and fine features, thus proving the solution to the tissue variability. The proposed method significantly improves the segmentation performance compared to the state-of-the-art methods on three different benchmark datasets. We demonstrate that the proposed model is generalized for 20 cancer sites, more than any reported literature. The proposed model is less complex than most state-of-the-art models. The impact of the proposed model is that it will help improve further procedures such as nuclei instance segmentation, nuclei classification, and cancer grading.

Ibtihaj Ahmad

Numerous initiatives to rely on new renewable energy sources, such solar electricity, have been sparked by the increased interest in global warming. With an increase in home photovoltaic (PV) panels that are available to the public, more precise calculations of energy generation are now possible. Segmenting satellite images offers a straightforward and inexpensive way to categorize solar panels..This work suggests a method for classifying and segmenting solar panels that combines the watershed algorithm with deep learning approaches. First, a Convolutional Neural Network (CNN) architecture with the ResNet, EfficientNet, and Inception architectures is used for classification. Through the fine-tuning of pre-trained networks on a heterogeneous dataset of solar panels, transfer learning improves performance. The categorization model recognizes solar panels in a variety of settings with accuracy, making maintenance and monitoring easier. After classification, the watershed method uses intensity gradients to precisely delineate solar panels from the background. Tasks like defect detection and layout optimization are made easier when deep learning-based classification and watershed segmentation are combined. The outcomes of the experiments show how well the suggested method performs in terms of segmenting and classifying solar panels under various circumstances. A flexible automated solar panel management solution is provided by the combination of deep learning and the watershed algorithm, which promotes increased sustainability and efficiency in solar energy systems.

BAKKA ARUN KUMAR