Determination of Optimal Toxic Concentration and Accumulation of Cadmium in Broiler Chicks

Fazli Subhan,Ayaz Khan,Fazli Wahid,Adeeb Shehzad,Amin Ullah Jan 한국독성학회 2011 Toxicological Research Vol.27 No.3

Cadmium is considered one of the most toxic, non biodegradable heavy metal for the human and animals. The purpose of the present study was to investigate the changes in biochemical parameters of blood and accumulation of cadmium in various tissue caused by various levels of dietary cadmium chloride (CdCl₂) in broiler chicks. CdCl₂ was administered through drinking water to broiler chicks. In spectral analysis, CdCl₂ treatment caused a significant increase in Glutamate pyruvate transaminase (GPT), creatinine and uric acid levels in all treated groups. Intriguingly, the GPT, creatinine, and uric acid levels were significantly higher at 75 ㎎/㎏ as compared to the groups treated with high doses (100, 125 and 150 ㎎/㎏) of CdCl₂. Atomic Absorption Spectrophotometer (AAS) was used for the determination of Cd accumulation in kidney, liver and Breast muscles. AAS analysis revealed that Cd accumulation is increased in breast muscles as compared to liver and kidney at higher doses of Cd than 75 ㎎/㎏.

Determination of Optimal Toxic Concentration and Accumulation of Cadmium in Broiler Chicks

Subhan, Fazli,Khan, Ayaz,Wahid, Fazli,Shehzad, Adeeb,Jan, Amin Ullah Korean Society of ToxicologyKorea Environmental Mu 2011 Toxicological Research Vol.28 No.3

Cadmium is considered one of the most toxic, non biodegradable heavy metal for the human and animals. The purpose of the present study was to investigate the changes in biochemical parameters of blood and accumulation of cadmium in various tissue caused by various levels of dietary cadmium chloride ($CdCl_2$) in broiler chicks. $CdCl_2$ was administered through drinking water to broiler chicks. In spectral analysis, $CdCl_2$ treatment caused a significant increase in Glutamate pyruvate transaminase (GPT), creatinine and uric acid levels in all treated groups. Intriguingly, the GPT, creatinine, and uric acid levels were significantly higher at 75 mg/kg as compared to the groups treated with high doses (100, 125 and 150 mg/kg) of $CdCl_2$. Atomic Absorption Spectrophotometer (AAS) was used for the determination of Cd accumulation in kidney, liver and Breast muscles. AAS analysis revealed that Cd accumulation is increased in breast muscles as compared to liver and kidney at higher doses of Cd than 75 mg/kg.

Hybrid Indoor Position Estimation using K-NN and MinMax

( Fazli Subhan ),( Shakeel Ahmed ),( Sajjad Haider ),( Sajid Saleem ),( Asfandyar Khan ),( Salman Ahmed ),( Muhammad Numan ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9

Due to the rapid advancement in smart phones, numerous new specifications are developed for variety of applications ranging from health monitoring to navigations and tracking. The word indoor navigation means location identification, however, where GPS signals are not available, accurate indoor localization is a challenging task due to variation in the received signals which directly affect distance estimation process. This paper proposes a hybrid approach which integrates fingerprinting based K-Nearest Neighbors (K-NN) and lateration based MinMax position estimation technique. The novel idea behind this hybrid approach is to use Euclidian distance formulation for distance estimates instead of indoor radio channel modeling which is used to convert the received signal to distance estimates. Due to unpredictable behavior of the received signal, modeling indoor environment for distance estimates is a challenging task which ultimately results in distance estimation error and hence affects position estimation process. Our proposed idea is indoor position estimation technique using Bluetooth enabled smart phones which is independent of the radio channels. Experimental results conclude that, our proposed hybrid approach performs better in terms of mean error compared to Trilateration, MinMax, K-NN, and existing Hybrid approach.

An Evaluation of Automated Tumor Detection Techniques of Brain Magnetic Resonance Imaging (MRI)

Fazli Wahid,Muhammad Fayaz,Abdul Salam Shah 보안공학연구지원센터 2016 International Journal of Bio-Science and Bio-Techn Vol.8 No.2

Image processing is a technique developed by computer and Information technology scientist and being used in all field of research including medical sciences. The focus of this paper is the use of image processing in tumor detection from the brain Magnetic Resonance Imaging (MRI). For the brain tumor detection, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are the prominent imaging techniques, but most of the experts prefer MRI over CT. The traditional method of tumor detection in MRI images is a manual inspection which provides variations in the results when analyzed by different experts, therefore, in view of the limitations of the manual analysis of MRI, there is a need for an automated system that can produce globally acceptable and accurate results. There is enough amount of published literature available to replace the manual inspection process of MRI images with the digital computer system using image processing techniques. In this paper, we have provided a review of digital image processing techniques in the context of brain MRI processing and critically analyzed them for the identification of the gaps and limitations of the techniques so that the gaps can be filled and limitations of various techniques can be improved for precise and better results.

A Prediction Approach for Demand Analysis of Energy Consumption Using K-Nearest Neighbor in Residential Buildings

Fazli Wahid,DoHyeun Kim 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.2

In order to manage efficiently the energy production, storage and management system, it is very important to analyze accurately the energy requirements for residential sector because the residential sector consumes a considerable amount of total energy produced. The main aim of the paper is the assurance of energy production according to the consumer demands in an efficient manner. The energy market is an important tool for setting prices between the energy producers, suppliers and the consumers. An excellent precision in the prediction of next day consumption is required for the suppliers to get good prices in the energy traded. The main aim of this paper is to facilitate the energy suppliers to make decisions for the provision of energy to different apartments according to their demand. In this paper, we have utilized K-Nearest Neighbors classifier for daily energy consumption prediction based on classification. The process consists of five stages namely data collection, data processing, prediction, and validation and performance evaluation. The historical data containing hourly consumption of 520 apartments of Seoul, Republic of Korea has been used in the experimentation. The data has been divided into different training and testing ratios and different qualitative and quantitative measures have been applied to find the performance and efficiency of the predictor. The highest accuracy has been observed for 60-40% training and testing ratio giving 95.9615% accurate results. The effectiveness of the model has been validated using 10-Fold and 5-Fold cross validation.

An Efficient Artificial Intelligence Hybrid Approach for Energy Management in Intelligent Buildings

( Fazli Wahid ),( Lokman Hakim Ismail ),( Rozaida Ghazali ),( Muhammad Aamir ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.12

Many artificial intelligence (AI) techniques have been embedded into various engineering technologies to assist them in achieving different goals. The integration of modern technologies with energy consumption management system and occupant’s comfort inside buildings results in the introduction of intelligent building concept. The major aim of this integration is to manage the energy consumption effectively and keeping the occupant satisfied with the internal environment of the building. The last few couple of years have seen many applications of AI techniques for optimizing the energy consumption with maximizing the user comfort in smart buildings but still there is much room for improvement in this area. In this paper, a hybrid of two AI algorithms called firefly algorithm (FA) and genetic algorithm (GA) has been used for user comfort maximization with minimum energy consumption inside smart building. A complete user friendly system with data from various sensors, user, processes, power control system and different actuators is developed in this work for reducing power consumption and increase the user comfort. The inputs of optimization algorithms are illumination, temperature and air quality sensors’ data and the user set parameters whereas the outputs of the optimization algorithms are optimized parameters. These optimized parameters are the inputs of different fuzzy controllers which change the status of different actuators according to user satisfaction.

Synthesis of Multifunctional Organic Nanoparticles Combining Photodynamic Therapy and Chemotherapeutic Drug Release

Fazli Sozmen,Merve Kucukoflaz,Mustafa Ergul,Zeynep Deniz Sahin Inan,Yasemin Bozkurt,Dilsad Taydas 한국고분자학회 2022 Macromolecular Research Vol.30 No.1

Cancer is a group of diseases that are caused by uncontrolled proliferation of cells in various parts of the body and it is one of the most studied diseases worldwide. Photodynamic therapy (PDT) is a treatment that uses photosensitizers called photosensitizing agents in addition to light to kill cancer cells. Photosensitizers work only after they have been activated by certain types of light. PDT contains three basic components, these are a photosensitizer, visible light and molecular oxygen (3O2). The efficiency of the therapeutic action is directly related to the property of the photosensitizer. In this study, chitosan and BODIPY based nanoparticles that were capable of carrying out drug delivery and producing singlet oxygen (1O2) were synthesized for the first time. For this purpose, organic nanoparticles showed PDT feature were synthesized via the formation of ionic complexes formed with opposite charged ionic interactions between the synthesized BODIPY derivative (PDT agent) and chitosan hydrochloride at appropriate pH (pH=6). Later, during the formation of this ionic complex, a chemotherapeutic model drug (Doxorubicin) was added to the medium and chemotherapeutic drug-loaded chitosan and BODIPY-based nanoparticles were synthesized. Finally, while drug-free (Y1-Chitosan nanoparticles) and drug-loaded organic nanoparticles (Y1-Chitosan-Dox nanoparticles) showed very good PDT properties, they were found to be effective on MCF7 cancer cells and less toxic to L929 cells.

Using Probabilistic Classification Technique and Statistical Features for Brain Magnetic Resonance Imaging (MRI) Classification: An Application of AI Technique in Bio-Science

Fazli Wahid,Rozaida Ghazali,Muhammad Fayaz,Abdul Salam Shah 보안공학연구지원센터 2016 International Journal of Bio-Science and Bio-Techn Vol.8 No.6

There are many medical imaging modalities used for the analysis and cure of various diseases. One of the most important of these modalities is Magnetic Resonance Imaging (MRI). MRI is advantageous over other modalities due to its high spatial resolution and the excellent capability of discrimination of soft tissues. In this paper, an automated classification approach of normal and pathological MRI is proposed. The proposed model three simple stages; preprocessing, feature extraction and classification. Two types of features; color moments and texture features have been considered as main features for the description of brain MRI. A probabilistic classifier based on logistic function has been used for the MRI classification. A standard data set consisting of one hundred and fifty images has been used in the experiments, which was divided into 66% training and 34% testing. The proposed approach gave 98% accurate results for training data set and 94% accurate results for the testing data set. For validation of the proposed approach, 10-Fold cross validation was applied, which gave 90.66% accurate results. The classification capability of probabilistic classifier has been compared with the different state of art classifiers, including Support Vector Machine (SVM), Naïve Bayes, Artificial Neural Network (ANN), and Normal densities based linear classifier.

Brain Computer Interfacing: A Multi-Modal Perspective

Fazli, Siamac,Lee, Seong-Whan Korean Institute of Information Scientists and Eng 2013 Journal of Computing Science and Engineering Vol.7 No.2

Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.

Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces

Fazli, Siamac,Dahne, Sven,Samek, Wojciech,Bieszmann, Felix,Muller, Klaus-Robert IEEE 2015 Proceedings of the IEEE Vol.103 No.6

<P>Brain-computer interfaces (BCIs) are successfully used in scientific, therapeutic and other applications. Remaining challenges are among others a low signal-to-noise ratio of neural signals, lack of robustness for decoders in the presence of inter-trial and inter-subject variability, time constraints on the calibration phase and the use of BCIs outside a controlled lab environment. Recent advances in BCI research addressed these issues by novel combinations of complementary analysis as well as recording techniques, so called hybrid BCIs. In this paper, we review a number of data fusion techniques for BCI along with hybrid methods for BCI that have recently emerged. Our focus will be on sensorimotor rhythm-based BCIs. We will give an overview of the three main lines of research in this area, integration of complementary features of neural activation, integration of multiple previous sessions and of multiple subjects, and show how these techniques can be used to enhance modern BCI systems.</P>