Impact on Requirement Elicitation Process when Transforming Software from Product Model to a Service Model

Sameen Fatima,Amna Anwer,Adil Tareen International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8

Influential trend that widely reflected the software engineering industry is service oriented architecture. Vendors are migrating towards cloud environment to benefit their organization. Companies usually offer products and services with a goal to solve problems at customer end. Because customers are more interested in solution of their problem rather than focusing on products or services. In software industry the approach in which customers' problems are solved by providing services is known as software as a service. However, software development life cycle encounters enormous changes when migrating software from product model to service model. Enough research has been done on the overall development process but a limited work has been done on the factors that influence requirements elicitation process. This paper focuses on those changes that influence requirement elicitation process and proposes a systematic methodology for transformation of software from product to service model in a successful manner. The paper then elaborates the benefits that inherently come along with elicitation process in cloud environment. The paper also describes the problems during transformation. The paper concludes that requirement engineering process turn out to be more profitable after transformation of traditional software from product to service model.

A brief review on graphene applications in rechargeable lithium ion battery electrode materials

Sameen Akbar,Muhammad Rehan,Liu Haiyang,Iqra Rafique,Hurria Akbar 한국탄소학회 2018 Carbon Letters Vol.28 No.-

Graphene is a single atomic layer of carbon atoms, and has exceptional electrical, mechanical, and optical characteristics. It has been broadly utilized in the fields of material science, physics, chemistry, device fabrication, information, and biology. In this review paper, we briefly investigate the ideas, structure, characteristics, and fabrication techniques for graphene applications in lithium ion batteries (LIBs). In LIBs, a constant three-dimensional (3D) conductive system can adequately enhance the transportation of electrons and ions of the electrode material. The use of 3D graphene and graphene-expansion electrode materials can significantly upgrade LIBs characteristics to give higher electric conductivity, greater capacity, and good stability. This review demonstrates several recent advances in graphenecontaining LIB electrode materials, and addresses probable trends into the future.

A Novel Road Segmentation Technique from Orthophotos Using Deep Convolutional Autoencoders

Sameen, Maher Ibrahim,Pradhan, Biswajeet The Korean Society of Remote Sensing 2017 大韓遠隔探査學會誌 Vol.33 No.4

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

A Novel Road Segmentation Technique from Orthophotos Using Deep Convolutional Autoencoders

( Maher Ibrahim Sameen ),( Biswajeet Pradhan ) 대한원격탐사학회 2017 大韓遠隔探査學會誌 Vol.33 No.4

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model`s parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size 106 × 106 pixels, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods

Lee, Jung-Hyun,Sameen, Maher Ibrahim,Pradhan, Biswajeet,Park, Hyuck-Jin Elsevier 2018 Geomorphology Vol.303 No.-

<P><B>Abstract</B></P> <P>This study evaluated the generalizability of five models to select a suitable approach for landslide susceptibility modeling in data-scarce environments. In total, 418 landslide inventories and 18 landslide conditioning factors were analyzed. Multicollinearity and factor optimization were investigated before data modeling, and two experiments were then conducted. In each experiment, five susceptibility maps were produced based on support vector machine (SVM), random forest (RF), weight-of-evidence (WoE), ridge regression (Rid_R), and robust regression (RR) models. The highest accuracy (<I>AUC</I> =0.85) was achieved with the SVM model when either the full or limited landslide inventories were used. Furthermore, the RF and WoE models were severely affected when less landslide samples were used for training. The other models were affected slightly when the training samples were limited.</P>

Antioxidant potential of buffalo and cow milk Cheddar cheeses to tackle human colon adenocarcinoma (Caco-2) cells

Nuzhat Huma,Saima Rafiq,Aysha Sameen,Imran Pasha,Muhammad Issa Khan 아세아·태평양축산학회 2018 Animal Bioscience Vol.31 No.2

Objective: The aim of present study was to assess the anti-oxidant potential of water-soluble peptides (WSPs) extract derived from buffalo and cow milk Cheddar cheeses at different stages of ripening. Methods: The antioxidant potential of WSPs extract was assessed through 2,2'-azinobis-3-ethylbenzothiazoline-6sulfonic acid (ABTS)-radical scavenging activity. In addition, impact of WSPs extract on cell viability and production of reactive oxygen species (ROS) in human colon adenocarcinoma Caco-2 (tert-butylhydroperoxide-induced) cell lines was also evaluated. Results: The ABTS-radical scavenging activity increased progressively with ripening period and dose-dependently in both cheeses. However, peptide extract from buffalo milk Cheddar cheese demonstrated relatively higher activity due to higher contents of water-soluble nitrogen. Intracellular ROS production in Caco-2 cells decreased significantly (p<0.05) till 150th day of cheese ripening and remained constant thereafter. Additionally, dose-dependent response of WSPs extract on antioxidant activity was noticed in the Caco-2 cell line. Conclusion: On the basis of current in vitro study, the Cheddar cheese WSPs extract can protect intestinal epithelium against oxidative stress due to their antioxidant activity.

Chemical Composition, Nitrogen Fractions and Amino Acids Profile of Milk from Different Animal Species

Rafiq, Saima,Huma, Nuzhat,Pasha, Imran,Sameen, Aysha,Mukhtar, Omer,Khan, Muhammad Issa Asian Australasian Association of Animal Productio 2016 Animal Bioscience Vol.29 No.7

Milk composition is an imperative aspect which influences the quality of dairy products. The objective of study was to compare the chemical composition, nitrogen fractions and amino acids profile of milk from buffalo, cow, sheep, goat, and camel. Sheep milk was found to be highest in fat ($6.82%{\pm}0.04%$), solid-not-fat ($11.24%{\pm}0.02%$), total solids ($18.05%{\pm}0.05%$), protein ($5.15%{\pm}0.06%$) and casein ($3.87%{\pm}0.04%$) contents followed by buffalo milk. Maximum whey proteins were observed in camel milk ($0.80%{\pm}0.03%$), buffalo ($0.68%{\pm}0.02%$) and sheep ($0.66%{\pm}0.02%$) milk. The non-protein-nitrogen contents varied from 0.33% to 0.62% among different milk species. The highest r-values were recorded for correlations between crude protein and casein in buffalo (r = 0.82), cow (r = 0.88), sheep (r = 0.86) and goat milk (r = 0.98). The caseins and whey proteins were also positively correlated with true proteins in all milk species. A favorable balance of branched-chain amino acids; leucine, isoleucine, and valine were found both in casein and whey proteins. Leucine content was highest in cow ($108{\pm}2.3mg/g$), camel ($96{\pm}2.2mg/g$) and buffalo ($90{\pm}2.4mg/g$) milk caseins. Maximum concentrations of isoleucine, phenylalanine, and histidine were noticed in goat milk caseins. Glutamic acid and proline were dominant among non-essential amino acids. Conclusively, current exploration is important for milk processors to design nutritious and consistent quality end products.

Development and Mechanical Characterization of Weave Design Based 2D Woven Auxetic Fabrics for Protective Textiles

Mumtaz Ali,Muhammad Zeeshan,Muhammad Bilal Qadir,Rabia Riaz,Sheraz Ahmad,Yasir Nawab,Aima Sameen Anjum 한국섬유공학회 2018 Fibers and polymers Vol.19 No.11

Auxetic materials expand in at least one dimension, when stretched longitudinally i.e. they have negative Poisson’s ratio. Development of 2D woven auxetic fabrics (AF) is a new approach to develop mechanically stable auxetic textile structures. However, the mechanical response of such emerging structure is still not studied in detail yet, therefore different mechanical properties of 2D woven AF are compared with conventional non-auxetic fabric (NAF). AF was developed by orienting yarns in auxetic honey-comb (AHC) geometry and auxeticity is induced due to such orientation of yarns. AF was developed using conventional (non-auxetic) materials; cotton yarn and elastane cotton yarn in warp and weft dimension respectively, using air jet loom. Structure and auxeticity of AF were analyzed using a digital microscope and its different mechanical properties (tensile strength, tear strength, bursting strength, cut resistance, and puncture resistance) were studied. AF showed superior mechanical properties with a lower initial modulus, which is beneficial for different protective textiles applications like cut resistance gloves, blast resistant curtains, and puncture tolerant elastomeric composites.

Self-assembled nitrogen-doped graphene quantum dots (N-GQDs) over graphene sheets for superb electro-photocatalytic activity

Riaz, Rabia,Ali, Mumtaz,Sahito, Iftikhar Ali,Arbab, Alvira Ayoub,Maiyalagan, T.,Anjum, Aima Sameen,Ko, Min Jae,Jeong, Sung Hoon Elsevier BV * North-Holland 2019 Applied Surface Science Vol.480 No.-

<P><B>Abstract</B></P> <P>Nitrogen-doped graphene quantum dots (N-GQDs) are emerging electroactive and visible light active organic photocatalysts, known for their high stability, catalytic activity and biocompatibility. The edge surfaces of N-GQDs are highly active, however, when N-GQDs make the film the edges are not fully exposed for catalysis. To avoid this issue, the N-GQDs are shaped to branched leaf shape, with an extended network of voids, offering highly active surfaces (edge) exposed for electrocatalytic and photocatalytic activity. The nitrogen doping causes a decrease in the bandgap of N-GQDs, thus enabling them to be superb visible light photocatalyst, for degradation of Methylene blue dye from water. Photoluminescence results confirmed that by a synergistic combination of the highly conductive substrate; Carbon fabric coated graphene sheets (CF-rGO) the recombination of photogenerated excitons is significantly suppressed, hence enabling their efficient utilization for catalysis. Comparatively, uniformly coated N-GQDs showed 49.3% lower photocatalytic activity, owing to their hidden active sites. The degradation was further boosted by 30% by combining the electrocatalytic activity, i.e. electro-photocatalysis of the proposed electrode. The proposed electrode material was analyzed using TEM, FE-SEM, FTIR, AFM, and WA-XRD, whereas the stability of electrode was confirmed by TGA, tensile test, bending test, and in harsh chemical environments. The proposed photo-electrocatalyst electrode is binder-free, stable, flexible and highly conductive, which makes the electrode quite suitable for flexible catalytic devices like flexible solar cells and wearable supercapacitors.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A flexible electrode is fabricated using self-assembled overlayer of Nitrogen doped Graphene Quantum Dots (N-GQDs). </LI> <LI> Self-assembeled highly porous leaflets structure has maximum exposed edge surfaces to accelarate the catalytic reaction. </LI> <LI> The proposed electrode is metal free and is stable at high temperature, harsh chemical environments, and mechanical stresses. </LI> <LI> The surface resistance of the all carbon electrode is only 2.5 Ω sq.<SUP>−1</SUP>. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>Nitrogen doped graphene quantum dots (N-GQDs) were self-assembled (with high porosity) on reduced graphene oxide coated carbon fabric to fabricate a highly stable visible light photocatlytically and electrocatalytically active flexible electrode for water treatment.</P> <P>[DISPLAY OMISSION]</P>