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Journal of Artificial Intelligence
eISSN: 2077-2173
pISSN: 1994-5450

Editor-in-Chief:  Dr. Santosh Kumar Nanda
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Mini Review
Feature Selection from Biological Database for Breast Cancer Prediction and Detection Using Machine Learning Classifier
Abhineet Gupta and Baij Nath Kaushik
Cancer is one of the diagnostic threats appearing to the mankind in this century and among various cancers, breast cancer is the major death causing disease which occurs mainly in women belonging to age between 45 and 60. Early detection and its appropriate treatment can significantly reduce the chances of their death. The objective of this review paper was to study the current systems to develop models with higher classification accuracy for prediction of breast cancer symptoms, their chances of recurrence at the early stage and also their chances of survivability. Here investigation was also done to verify whether comparable accuracy can be achieved even with lesser number of features or not. Initially the feature set is reduced to avoid the over fitting problem and then various machine learning techniques are applied. Here, three different types of feature selection techniques and various machine learning classifiers have been discussed. Further, the comparative analysis among feature selection methods has been done based on their accuracy, computational speed and their dependency on machine learning classifiers. Moreover, the advantages and disadvantages of various classifiers are also discussed. A study of different results from past years have been compared based on the applied classifier, feature selection technique, number of features used and different performance measures like accuracy, sensitivity etc. From different research studies, it is found that comparable accuracy can be achieved even with lesser number of features, which overall reduces the computational complexity of the model. It have discovered that different researchers have found the optimal number of features by hit and trial method which is a very difficult task and to overcome this difficulty, the future scope has been discussed.
Mini Review
Devanagari and Gurmukhi Script Recognition in the Context of Machine Learning Classifiers
Reya Sharma, Baij Nath Kaushik and Naveen Kumar Gondhi
The handwritten character recognition is potentially an active area of research due to the presence of several challenging issues. Due to a large variation in writing styles, development of optical handwritten character reader is a challenging task. In order to decrease the burden of computation and to improve the recognition accuracy, several measures need to be taken in the overall process of recognition. The main objective of this review was to recognize and analyze handwritten document images. There are wide varieties of classification techniques available for the problem of pattern recognition. These techniques include Support Vector Machine (SVM), Back Propagation Neural Networks (BPNN), Probabilistic Neural Networks (PNN) and many more. In this study, a survey has been performed on some of these machine learning techniques for the identification of various handwritten north Indian scripts. This study attempts to address the most significant results obtained so far and then all the gathered data is represented in the form of tables so as to have a clear idea by visualizing data at once. This research paper provides a comprehensive survey on various machine learning techniques involved in north Indian script recognition and the study also highlights the crucial aspects of the research till date.
Research Article
Multi-resident Activity Recognition Method Based in Deep Belief Network
Nadia Oukrich, El Bouazaoui Cherraqi, Abdelilah Maach and Driss Elghanami
Background and Objective: Existing work on human activity recognition mainly focuses on recognizing activities for a single resident. However, in real life, activities are often performed by multiple users. This study aimed to recognize multiple resident activities inside home using deep neural networks and an ontological approach for features selection. Materials and Methods: This model comprised an ontological approach method for robust features extraction and selection, a Deep Belief Network (DBN) algorithm for recognising three categories of multiple resident activities inside home. A simulated experiment was conducted using publicly two multiple resident CASAS databases collected at Washington State University (WSU) and the proposed approach was compared with traditional recognition approaches such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). Results: The results showed that the proposed approach based on DBN and ontology produce better accuracy results compared to SVM and ANN. Conclusion: In this research, deep neural network algorithm had been successfully developed to recognize daily life human activities using features manually extracted.
Research Article
Investigation of an Efficient RF-MEMS Switch for Reconfigurable Antenna Using Hybrid Algorithm with Artificial Neural Network
Qazi Fasihuddin Zahuruddin and Mulpuri Sri Rukmini
Background and Objective: As MEMS (Micro Electro Mechanical System) technology continuously growing, MEMS for reconfigurable antenna design and optimization is becoming an interesting and important research issue. Many RF-MEMS reconfigurable antenna design focused on changing operating frequency while sustaining their radiation characteristics. However, to enhance the performance of the antenna it was proposed to change the radiation characteristics and keeping the operating frequency constant. Thus for achieving this, the main objective of this investigation study was to design efficient RF-MEMS switch for reconfigurable antenna using hybrid optimization algorithm with Artificial Neural Network. Materials and Methods: In this proposed research method, for optimization of RF-MEMS switch, parameters like beam length, beam width, switch thickness, torsion arm thickness, holes and gaps are considered and to get optimized parameters, gravitational search optimization algorithm is intended with artificial neural network which has been implemented on the working platform of MATLAB. Results: The simulated result indicates that the hybrid algorithm enhanced the global search ability and gives the reasonably good accuracy and reduction in mean square error and Bit error rate. Conclusion: Finally after comparing our proposed technique with existing techniques, concluding that we are getting efficient RF-MEMS switch for reconfigurable antenna and performance of the system is increasing.
Research Article
Impact of Information and Communication Technology Usage on Work-life Balance among Professional Women in the Construction Industry
Alireza Jalali and Mastura Jaafar
Background and Objective: Women’s entry into the male-dominated industry, such as the construction industry has been rather slow due to family responsibility. With that, the main objective of this study was to investigate the level and the effect of Information and Communication Technology (ICT) usage on the satisfaction of work-life balance among professional women in the construction industry. Materials and Methods: Questionnaires were administered to 35 professional women in the construction industry. A model linked with ICT usage to measure satisfaction of work-life balance was tested using Smart PLS M2 Version 2.0. Both measurement and structural models were tested and the results turned out to be positive. Results: The structural model in PLS analysis displayed a positive relationship between ICT usage and satisfaction level of work-life balance. Conclusion: The ICT usage increased the satisfaction of work-life balance and allowed women to get in touch with their families, apart from minimizing their burden some workload.

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