Computer Science

The best way to develop our personal and professional development  is by changing ourselves according to situation and by organizing or improving life of  our environment  by learning and self improvement. Here by using my personal traits in this report I am going to describe the benefits and all aspect of personal and professional development  impact on our life. Nowadays the need of personal and professional development is universal  whether they have different profession. Professionals mainly increase their knowledge and their experience in professionalism over the aspects of their work based on current research based practice. According to my point of view higher education study is not about increasing the book knowledge and gaining critical and analytical knowledge of respective subject but it is more important the applicability of the knowledge which is mainly gain by practical knowledge. In present scenario job are not longer for life . at this time knowledge and skill are increasing at very fast rate where we have to adaptable and flexible according to the needs. And we have to prepare for taking responsibility for lifelong skill. It is very important for both personal and professional development.

Nowadays new fuels and many different vehicles is very much required to decrease the dependency on foreign oil and decrease the transportation by carbon presence. Natural gas and other fuels decrease oil consumption but we can see that this temporary fuel is best. That why we can say that electricity and hydrogen can become our carbon free transportation energy. Most of the people know what is electric car. A car which run on battery powered electric motor. It has not combustion engine and its uses electricity than gasoline. It doesn't uses fossil fuels that why it does not produce any pollution. whereas fuel cell car is also a electric car. Its run on a motor powered by electricity. In this device takes hydrogen which is most available in the universe and produce electricity from it. So we can say that fuel cell is a type of battery which can make electricity on the fly. The green thing and big thing is that in both fuel cell car and electric car cant produce any pollution. The fuel on which electric cars run on are electricity whereas fuel cell run on hydrogen. So here truth is that both fuel cell cars and electric cars have potential to produce non polluting transportation. So we can say that fuel cell and electric cars are perfect form of non-polluting and green form in the sector of transportation. A fuel cell can carry hydrogen in a tank box, and mix with oxygen from the air to generate electricity. As we see that, it is different type of electric car, where there are no batteries available in them. Instead, the power is consist in the form of hydrogen, which making refilling as simple as like as a diesel and petrol car. However, fuel cell can give the electric energy a run a lot of money in the future. In recent research development has give a big and great possibilities for new catalysts available than the expensive platinum material. Many big manufacturer companies have made great advancement in technology which has decreases the size and cost of fuel cells car. There are many more breakthroughs. but electric cars are still a mile ahead because it is very hard and difficult to make a hydrogen fuelling network all over the world. which may be change as soon as possible. The artificial photosynthesis is lie between 15% and 25% which is not a very big percentage. If it run at the same present efficiency, then the artificial photosynthesis becomes a best and good alternative than any other natural gas for making hydrogen as a fuel. Fuel cell is now become a very good and tough competitor to electric cars, because in this it is very cheaper to build many charging stations. As we see that we really don't know when this day will come. When we reach to our social goals : a. Due to 80% decrease in greenhouse effect near 1990 levels available in

Re-usability of previous designs is very tough when we are providing best-practice solutions in the corporations. Making such types of designs provides important guidance for Enterprise Architects. However, cataloging such blueprints for reuse is a tough task, and processes to perform cataloging have yet to be formalized and standardized. Big corporations mainly invest in developing and standardizing such processes internally, which typically results in a variety of possible cataloging approaches which are specific to the industry or enterprise.

As we know that spy satellite is a form of communication satellite, which is mainly used for defence or various other intelligence motives. Low Earth orbit satellite network basically used for telecommunication medium. These satellites rotates in the closest orbit near earth's surface which mainly range from 400 to 1000 miles. Due to its fast speed satellites mainly used for emails, video conferencing and various other communication services. There are so many different Communication protocols are developed in the systems to make various types of digital message formats and their rules. The basic aim behind communication protocol is to transffer messages between different computing devices and provide better telecommunication facilities.  In current time information system security implementation recommendation used to find out the threat or securities vulnerabilities on the basis of processes, users and infrastructure.

As people have become more aware of their actions and how they affect their surroundings, they have realized the dire state of the environment. As a result, the recycling movement has gained momentum as a measure to save it. Contemporarily, the recycling industry has not seen a major shift and the problems that existed decades earlier persist. Trash classification is at the core of these problems, because if you can't classify it, obviously you can't recycle it. Manual classification often leads to misclassification as humans classify or judge things based on their experiences, knowledge, and not just absolute facts. Additionally, if the waste to be sorted is toxic, being in direct contact may be physically harmful to the people involved. Until a solution is found for this problem, the recycling industry won't be on par with the rise in recycling culture. Thus, this is the problem we have set out to solve; this paper proposes a Deep Learning model based on EfficientNet Architecture that can classify different kinds of trash with an accuracy high enough to make it a viable solution for the industry while using a comparatively lower number of parameters than existing methods. We achieved an accuracy of 98% on the TrashNet dataset, the standard data for Trash classification, and outperformed all existing models. Additionally, as no large dataset with a varied set of trash images was present, we created a new dataset of 8135 images by combining various datasets and standardized them, achieving a classification accuracy of 92.87% with EfficientNet B3.

Internet of things (IoT) devices are resource-constrained devices with limitations such as low computation power, low communication capabilities, low bandwidths, high latency and short lived power. Therefore, securing the communication among these devices is a key challenge for various sensitive applications. However, conventional encryption and decryption algorithms, known as ciphers, cannot be implemented because of their inherent complexities of implementation and power requirements. One of the promising options available is to implement lightweight ciphers for these resource constrained devices. Moreover, the choice of lightweight encryption tool has great dependency on the type of IoT devices being used in an application. In this paper, a lightweight cellular automata (CA) based cipher, named as Lightweight CA Cipher (LCC) has been proposed for IoT applications. In the proposed method, encryption is done at the perception layer, where the sensor nodes are deployed and decryption is done at the network layer where gateway devices are installed. Experimental results show that the proposed method is efficient than some of the existing ciphers like DES, 3DES when randomness, execution time and implementation simplicity are considered as prime requirements. This cipher passes the randomness tests as prescribed by National Institute of Standards and Technology (NIST), and it also passes all the DIEHARD tests and it establishes the security feature of LCC. Though it is specially designed for resource-constrained environments, it can be scaled up for a large number of sensor nodes.

Unerring driving decisions like obstacle avoidance, lane detection, conscious proximity of heavy vehicles, etc., are crucial for automated vehicles. This work proposes a strategy to detect heavy automation vehicles and uncertain objects which contribute in taking efficient and accurate driving decisions. A multi-task learning system utilizing rich visual features of a driving simulation game Moto Rider has been proposed in this work. The neural network used here learns an auxiliary task of driving a bike in any arbitrary unpredictable circumstance through reinforcement learning. Experimental results show that the proposed technique provides robustness in terms of safety for ‘ego-vehicle’ as well as the surroundings.

Since the future of the society depends upon the role of students and their services construct the prosperous and advanced society, so suitable career selection for the students' is considered to be an important problem to explore. As per psychology, if a student has the required capability, and positive attitudes towards a subject in terms of interest, attitude, memory, knowledge, environment, and career, then the student will achieve more in that subject. To consider this kind of uncertain issues, picture fuzzy set and rough set are found to be appropriate due to their inherent characteristics to deal with incomplete and imprecise information. In this study, picture fuzzy set and rough set-based approaches are proposed to help the student to choose an appropriate subject and consequently provide a good service or contribution to the society particularly in that domain. The main purpose of the article is to analyse student's features in terms of career, memory, interest, kn...

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accurate diagnosis and appropriate drug therapy to patients. This work aims to categorize electroencephalography signals on different channels' recordings for classifying and predicting epileptic seizures. The collection of the electroencephalography recordings contained in the dataset attributes 179 information and 11,500 instances. Instances are of five categories, where one is the symptoms of epilepsy seizure. We have used traditional, ensemble methods and deep machine learning techniques highlighting their performance for the epilepsy seizure detection task. One dimensional convolutional neural network, ensemble machine learning techniques like bagging, boosting (AdaBoost, gradient boosting, and XG boosting), and stacking is implemen...

Sufficient numbers of staff are required for any hospital for providing better services to patients with satisfactory treatment. In most hospitals, the patient's treatment and care depend on the availability of nurses. Less number of nurses are a reason for average care and treatment to patients, whereas more than the required number of nurses may cause wastage of expenditure and manpower. More importantly, an additional number of nurses may be assigned to some hospitals where a sufficient number of nurses are not available. Moreover, nurses are of different categories such as qualified nurses having experience in providing service to patients, qualified nurses without having experience, and nurses without qualification but have experience. The expenditure also depends on categories of nurses' appointments. To reach an equilibrium point in appointing nurses, we propose a hybridization model using linear regression, fuzzy set theory and game-theoretic approaches. Regression analysis is implemented for prediction based on different fuzzy membership values of independent variables. We implement two concepts of game theory, the Nash equilibrium and the perfect Nash equilibrium. Implementing the Nash equilibrium, different equilibria values are generated and by implementing perfect Nash equilibrium, a subgame is generated to reach one equilibrium value. We have illustrated the proposed approach using a case study in which the linear regression approach is implemented to predict the patients' arrival rate based on the monetary standard of the patient, communication facilities, patients curing chances, and patient choice towards the hospital, where these four features are quantified using fuzzy membership values. Nash equilibrium decides all possible ways of the nurses' allocations and perfect Nash equilibrium assist to reach an appropriate allocation. Thus, the hybridization of the regression approach, fuzzy membership and game theory approaches finalize the exact allocation of nurses. Finally, a comparative analysis is given to demonstrate the effectiveness of the proposed approach.

Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient's health condition, type of disease, and financial condition. Again, understaffing of nurses may hamper patient health and condition. Similarly, overstaffing of nurses is a waste of manpower. Adequate staffing of nurses is crucial. The authors propose a technique using game theory to meet overstaffing and understaffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, the final and most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital and critical decision points.

Sufficient numbers of staff are required for any hospital for providing better services to patients with satisfactory treatment. In most hospitals, the patient's treatment and care depend on the availability of nurses. Less number of nurses are a reason for average care and treatment to patients, whereas more than the required number of nurses may cause wastage of expenditure and manpower. More importantly, an additional number of nurses may be assigned to some hospitals where a sufficient number of nurses are not available. Moreover, nurses are of different categories such as qualified nurses having experience in providing service to patients, qualified nurses without having experience, and nurses without qualification but have experience. The expenditure also depends on categories of nurses' appointments. To reach an equilibrium point in appointing nurses, we propose a hybridization model using linear regression, fuzzy set theory and game-theoretic approaches. Regression analysis is implemented for prediction based on different fuzzy membership values of independent variables. We implement two concepts of game theory, the Nash equilibrium and the perfect Nash equilibrium. Implementing the Nash equilibrium, different equilibria values are generated and by implementing perfect Nash equilibrium, a subgame is generated to reach one equilibrium value. We have illustrated the proposed approach using a case study in which the linear regression approach is implemented to predict the patients' arrival rate based on the monetary standard of the patient, communication facilities, patients curing chances, and patient choice towards the hospital, where these four features are quantified using fuzzy membership values. Nash equilibrium decides all possible ways of the nurses' allocations and perfect Nash equilibrium assist to reach an appropriate allocation. Thus, the hybridization of the regression approach, fuzzy membership and game theory approaches finalize the exact allocation of nurses. Finally, a comparative analysis is given to demonstrate the effectiveness of the proposed approach.

Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analyzed the maximal speed up and parallel efficiency theoretically.

Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by OpenCV with Python. The data set we used is from Yale Face Database. We choose 136 images to analysis. When we run our algorithm we need to divide it into two phases. First, we need to train our algorithm. The purpose of this phase is to determine the minimum error which will be used in the next phase. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analyzed the maximal speed up and parallel efficiency theoretically.