Risk Management Policy of Telecommunication and Engineering Laboratory

Abdul Salam Shah,Muhammad Fayaz,Asadullah Shah,Shahnawaz Shah 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4

The Telecommunication laboratory plays an important role in carrying out research in the different fields like Telecommunication, Information Technology, Wireless Sensor Networks, Mobile Networks and many other fields. Every Engineering University has a setup of laboratories for students particularly for Ph.D. scholars to work on the performance analysis of different Telecommunication Networks including WLANs, 3G/4G, and Long Term Evolution (LTE). The laboratories help students to have hand on practice on the theoretical concepts they have learned during the teachings at the university. The technical subjects have a practical part also which boosts the knowledge of students and learning of new ideas. The Telecommunication and Engineering laboratories are equipped with different electronic equipment’s like digital trainers, simulators etc. and some additional supportive devices like computers, air conditioners, projectors, and large screens, with power backup facility that creates the perfect environment for experimentation. The setup of Telecommunication and Engineering laboratories cost huge amount, required to purchase equipment, and maintain the equipment. In any working environment risk factor is involved. To handle and avoid risks there must be risk management policy to tackle with accidents and other damages during working in the laboratory, may it be human or equipment at risk. In this paper, we have proposed a risk management policy for the Telecommunication and Engineering laboratories, which can be generalized for similar type of laboratories in engineering fields of studies.

Effect of Palestinian Honey on Spermatogenesis in Rats

Abdul-Salam Abdul-Ghani,Nabil Dabdoub,Rateb Muhammad,Rula Abdul-Ghani,Munir Qazzaz 한국식품영양과학회 2008 Journal of medicinal food Vol.11 No.4

Treatment of male albino rats with 5% honey for 20 days had no significant effect on total body weight or on the relative weight of other organs like the testis, seminal vesicles, spleen, kidneys, liver, heart, or brain. The only significant change was a 17% increase in the relative weight of the epididymis (P ≤ .01). The relative weight of all the other organs was similar to those in control animals treated for the same period with drinking water. Treatment of rats for the same period with the same concentration of 5% sucrose produced no significant changes in absolute or relative weight of tested organs compared to control animals. The same treatment with Palestinian honey increased significantly the epididymal sperm count by 37% (P ≤ .05). The activity of testicular marker enzymes for spermatogenesis such as sorbitol dehydrogenase (SDH) was increased by 31% (P ≤ .05), and lactate dehydrogenase (LDH) was reduced by 48% (P ≤ .05), which indicates that treatment with honey induces spermatogenesis. Similar treatment with sucrose had no significant effect on any of the key enzymes or epididymal sperm count. In conclusion, our results show that ingestion of honey induces spermatogenesis in rats by increasing epididymal sperm count, increasing selectively the relative weight of the epididymis, and increasing SDH activity and reducing LDH activity.

Time Series Crime Prediction Using a Federated Machine Learning Model

Salam, Mustafa Abdul,Taha, Sanaa,Ramadan, Mohamed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4

Crime is a common social problem that affects the quality of life. As the number of crimes increases, it is necessary to build a model to predict the number of crimes that may occur in a given period, identify the characteristics of a person who may commit a particular crime, and identify places where a particular crime may occur. Data privacy is the main challenge that organizations face when building this type of predictive models. Federated learning (FL) is a promising approach that overcomes data security and privacy challenges, as it enables organizations to build a machine learning model based on distributed datasets without sharing raw data or violating data privacy. In this paper, a federated long short- term memory (LSTM) model is proposed and compared with a traditional LSTM model. Proposed model is developed using TensorFlow Federated (TFF) and the Keras API to predict the number of crimes. The proposed model is applied on the Boston crime dataset. The proposed model's parameters are fine tuned to obtain minimum loss and maximum accuracy. The proposed federated LSTM model is compared with the traditional LSTM model and found that the federated LSTM model achieved lower loss, better accuracy, and higher training time than the traditional LSTM model.

Evaluation of Self-Healing Performance of Fiber Reinforced Mortar Incorporating PVA Fibers

Abdul Salam Buller(압둘 살람 불러),Fahad Ul Rehman Abro(파하드 울 레히만 아브로),Lim, Min-Ju(임민주),Lee, Kwang-Myong(이광명) 한국콘크리트학회 2017 한국콘크리트학회 학술대회 논문집 Vol.29 No.2

An Offline Signature Verification Technique Using Pixels Intensity Levels

Abdul Salam Shah,M.N.A. Khan,Fazli Subhan,Muhammad Fayaz,Asadullah Shah 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).

Testing Desktop Application : Police Station Information Management System

Abdul Salam Shah,Muhammad Fayaz,Asadullah Shah,Shahnawaz Shah 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.7

The police stations have adequate importance in the society to control the law and order situations of the country. In Pakistan, police stations manage criminal records and information manually. We have previously developed and improved a desktop application for the record keeping of the different registers of the police stations. The data of police stations is sensitive and that need to be handled within secured and fully functional software to avoid any unauthorized access. For the proper utilization of the newly developed software, it is necessary to test and analyze the system before deployment into the real environment. In this paper, we have performed the testing of an application. For this purpose, we have used Ranorex, automated testing tool for the functional and performance testing, and reported the results of test cases as pass or fail.

Navigation through Citation Network Based on Content Similarity Using Cosine Similarity Algorithm

Abdul Ahad,Muhammad Fayaz,Abdul Salam Shah 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.5

The rate of scientific literature has been increased in the past few decades; new topics and information is added in the form of articles, papers, text documents, web logs, and patents. The growth of information at rapid rate caused a tremendous amount of additions in the current and past knowledge, during this process, new topics emerged, some topics split into many other sub-topics, on the other hand, many topics merge to formed single topic. The selection and search of a topic manually in such a huge amount of information have been found as an expensive and workforce-intensive task. For the emerging need of an automatic process to locate, organize, connect, and make associations among these sources the researchers have proposed different techniques that automatically extract components of the information presented in various formats and organize or structure them. The targeted data which is going to be processed for component extraction might be in the form of text, video or audio. The addition of different algorithms has structured information and grouped similar information into clusters and on the basis of their importance, weighted them. The organized, structured and weighted data is then compared with other structures to find similarity with the use of various algorithms. The semantic patterns can be found by employing visualization techniques that show similarity or relation between topics over time or related to a specific event. In this paper, we have proposed a model based on Cosine Similarity Algorithm for citation network which will answer the questions like, how to connect documents with the help of citation and content similarity and how to visualize and navigate through the document.

Islam and Muslim Situation in Singapore : History and Current Issues

Sultan, Abdul Salam 이슬람문화연구소 1997 이슬람문화 Vol.1 No.1

Enhanced Biodegradation of Lindane Using Oil-in-Water Bio- Microemulsion Stabilized by Biosurfactant Produced by a New Yeast Strain, Pseudozyma VITJzN01

( Jaseetha Abdul Salam ),( Nilanjana Das ) 한국미생물 · 생명공학회 2013 Journal of microbiology and biotechnology Vol.23 No.11

Organochlorine pesticide residues continue to remain as a major environmental threat worldwide. Lindane is an organochlorine pesticide widely used as an acaricide in medicine and agriculture. In the present study, a new lindane-degrading yeast strain, Pseudozyma VITJzN01, was identified as a copious producer of glycolipid biosurfactant. The glycolipid structure and type were elucidated by FTIR, NMR spectroscopy, and GC-MS analysis. The surface activity and stability of the glycolipid was analyzed. The glycolipids, characterized as mannosylerythritol lipids (MELs), exhibited excellent surface active properties and the surface tension of water was reduced to 29 mN/m. The glycolipid was stable over a wide range of pH, temperature, and salinity, showing a very low CMC of 25 mg/l. Bio-microemulsion of olive oil-in-water (O/W) was prepared using the purified biosurfactant without addition of any synthetic cosurfactants, for lindane solubilization and enhanced degradation assay in liquid and soil slurry. The O/W bio-microemulsions enhanced the solubility of lindane up to 40-folds. Degradation of lindane (700 mg/l) by VITJzN01 in liquid medium amended with bio-microemulsions was found to be enhanced by 36% in 2 days, compared with degradation in 12 days in the absence of bio-microemulsions. Lindane-spiked soil slurry incubated with bio-microemulsions also showed 20-40% enhanced degradation compared with the treatment with glycolipids or yeast alone. This is the first report on lindane degradation by Pseudozyma sp., and application of bio-microemulsions for enhanced lindane degradation. MEL-stabilized bio-microemulsions can serve as a potential tool for enhanced remediation of diverse lindanecontaminated environments.

Predicting Brain Tumor Using Transfer Learning

Mustafa Abdul Salam,Sanaa Taha,Sameh Alahmady,Alwan Mohamed International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5

Brain tumors can also be an abnormal collection or accumulation of cells in the brain that can be life-threatening due to their ability to invade and metastasize to nearby tissues. Accurate diagnosis is critical to the success of treatment planning, and resonant imaging is the primary diagnostic imaging method used to diagnose brain tumors and their extent. Deep learning methods for computer vision applications have shown significant improvements in recent years, primarily due to the undeniable fact that there is a large amount of data on the market to teach models. Therefore, improvements within the model architecture perform better approximations in the monitored configuration. Tumor classification using these deep learning techniques has made great strides by providing reliable, annotated open data sets. Reduce computational effort and learn specific spatial and temporal relationships. This white paper describes transfer models such as the MobileNet model, VGG19 model, InceptionResNetV2 model, Inception model, and DenseNet201 model. The model uses three different optimizers, Adam, SGD, and RMSprop. Finally, the pre-trained MobileNet with RMSprop optimizer is the best model in this paper, with 0.995 accuracies, 0.99 sensitivity, and 1.00 specificity, while at the same time having the lowest computational cost.