Browsing by Author "Rahman, Md. Mosfikur"

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A New Chaotic-Based Analysis of Data Encryption and Decryption
(Springer, 2023-05-14) Johora, Fatema Tuj; -Ul-Islam, Alamin; Yesmin, Farzana; Rahman, Md. Mosfikur
Because the amount of exchange hypersensitive data via the Internet is growing at an exponential rate, network and data security have recently been the most pressing worry. On the subject of data security, many approaches are available, including “cryptography.” When data is sent from the sender to the receiver, it is encrypted using an encryption method, and when it is received by the receiver, it is decrypted using a decryption algorithm to see the exact and true data that was sent by the sender previously. Data encryption can be done in a number of ways. With algorithms like AES, DES, and RSA for data encryption and decryption, this research proposes a novel CRSA (chaotic random seed algorithm) technique. CRSA was also compared to other algorithms to see how well they performed. The experimental data presented in this work are used to examine those algorithms as well as our new CRSA algorithm. Cryptography, encryption, decryption, random reed, millisecond, and data security are all terms used in this paper.
Chronic Kidney Disease
(Daffodil International University, 2022-01-30) Rahman, Md. Mosfikur; Ahmed, Akash; Mahin, Mahfuja Ferdousi
Chronic Kidney Disease (CKD) is a term used in the medical profession to represent a range of illnesses that result in kidney damage or a decreased Glomerular Filtration Rate (GFR). In recent years, medical advances have made it feasible for doctors to treat this disease utilizing a variety of different methods, which they have done. Artificial intelligence and machine learning have gained popularity in recent years as a method of enhancing medical care and medical research in general, particularly in the field of medicine. Because Kidney Condition is a potentially fatal disease, it necessitates the application of machine learning to anticipate when it will develop in the first place. To forecast the development of "Chronic Kidney Disease," a broad variety of machine learning techniques, applications, and algorithms may be used in conjunction with one another. Using this method, a machine-learning algorithm generates a certain output, and the algorithm that outperformed all of the other algorithms is chosen as the best performance. This may make it possible for any physicians to identify the beginning of this disease as soon as the dialysis report is received. It is also possible to identify which component of the disease is the primary cause of the illness using this technique, which may be determined from the report study. It is necessary to use more complicated and dynamic algorithms in order to get the best possible outcome in this system, and these algorithms include Random Forest, Naïve Bayes, Decision Tree, K-Nearest Neighbor (KNN), XGBoost, AdaBoost, and others.
Current Update on Management Strategies for Neurological and Psychological disordersCyber Security Intruder Detection Using Deep Learning Approach
(Springer, 2022-11-29) Islam, Tariqul; Rahman, Md. Mosfikur; Saifuzzamam, Mohd.; Jabiullah, Md. Ismail
Intrusion detection systems (IDS) are among the most promising approaches for securing data and networks; through the years, numerous categorization algorithms have been utilized in IDS. In recent years, as the alarming increase in computer connectivity and the substantial number of applications associated with computer technology have increased, the challenge of cyber security is constantly rising. A proper system of protection for numerous cyber-attacks is also required. This is how incoherence and attacks in a computer network are detected and IDS developed, which could play a possible role in cyber security. The authors used the CICIDS2017 dataset to meet this objective. It is the 2017 set of the Canadian Cyber Security Institute. The authors propose an IDS based on the deep learning technique to increase safety. The purpose was to use a neural network classifier to predict the network and web attacks.
Future City of Bangladesh IOT
(IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), IEEE, 2020-12-26) Rahman, Md. Mosfikur; Kashem, Mohammad Abul; Mohiuddin, Mohammad; Hossain, Mohammad Alam; Moon, Nazmun Nessa
With the influence of science, the way people addressed challenges in the past has now changed. It's easier to make "stuff" smart now-to-day with the advancement of IoT. Thus, with the advancement of smart infrastructure and the emergence of the Internet of Things (IoT), it is possible to calculate real-time flood prediction in Bangladesh's sewerage system and also to track the ecosystem of underground tunnels easily and efficiently. This study represents the development of a sewerage system for an existing sewerage system in Bangladesh that can predict sewerage system overflow and implement a hazard condition monitoring system prototype using IoT technology by calculating risk factors such as calculating the amount of toxic gases in underground tunnels. We should save the lives of the employees who work in caves, contact workplaces and manhole underground work.