Muneer Ahmad

Sadia Basar, Mushtaq Ali, Abdul Waheed et al.

The motion or out-of-focus effect in digital images is the main reason for the blurred regions in defocused-blurred images. It may adversely affect various image features such as texture, pixel, and region. Therefore, it is important to detect in-focused objects in defocused-blurred images after the segmentation of blurred and non-blurred regions. The state-of-the-art techniques are prone to noisy pixels, and their local descriptors for developing segmentation metrics are also complex. To address these issues, this research, therefore, proposed a novel and hybrid-focused detection approach based on Discrete Cosine Transform (DCT) coefficients and PC Neural Net (PCNN) structure. The proposed approach partially resolves the limitations of the existing contrast schemes to detect in-focused smooth objects from the out-of-focused smooth regions in the defocus dataset. The visual and quantitative evaluation illustrates that the proposed approach outperformed in terms of accuracy and efficiency to referenced algorithms. The highest F-score of the proposed approach on Zhao's dataset is 0.7940 whereas on Shi's dataset is 0.9178.

Muneer Ahmad, Joseph U Igligli

This study employs scientometric methods to assess the research output and performance of the University of Nigeria from 2014 to 2023. By analyzing publication trends, citation patterns, and collaboration networks, the research aims to comprehensively evaluate the university's research productivity, impact, and disciplinary focus. These research endeavors are characterized by innovation, interdisciplinary collaboration, and commitment to excellence, making the University of Nigeria a significant hub for cutting-edge research in Nigeria and beyond. The present study has been undertaken to determine the impact of the university's research and publication trends from 2014 to 2023. The study focuses on year-wise research output, citation impact at local and global levels, prominent authors and their total output, top journals, collaborating countries, and the most contributing departments of the University of Nigeria. The university's ten years of publication data indicate that 6,353 papers were published from 2014 to 2023, receiving 86,202 citations with an h-index of 39. In addition to this, the stenographical mapping of data is presented through graphs using the VOSviewer software mapping technique. The findings of this study will contribute to understanding the university's research strengths, weaknesses, and potential areas for improvement. Additionally, the results will inform evidence-based decision-making for enhancing research strategies and policies at the University of Nigeria

Muneer Ahmad, M Sadik Batcha

The paper explores and analyses the trend of world literature on "Coronavirus Disease" in terms of the output of research publications as indexed in the Science Citation Index Expanded (SCI-E) of Web of Science during the period from 2011 to 2020. The study found that 6071 research records have been published on Coronavirus Disease till March 20, 2020. The various scientometric components of the research records published in the study period were studied. The study reveals the various aspects of Coronavirus Disease literature such as year wise distribution, relative growth rate, doubling time of literature, geographical wise, organization wise, language wise, form wise , most prolific authors, and source wise. The highest number of articles was published in the year 2019, while lowest numbers of research article were reported in the year 2020. Further, the relative growth rate is gradually increases and on the other hand doubling time decreases. Most of the research publications are published in English language and most of the publications published in the form of research articles. USA is the highest contributor to the field of Coronavirus Disease literature.

Muneer Ahmad, Dr M Sadik Batcha, S Roselin Jahina

Artificial intelligence has changed our day to day life in multitude ways. AI technology is rearing itself as a driving force to be reckoned with in the largest industries in the world. AI has already engulfed our educational system, our businesses and our financial establishments. The future is definite that machines with artificial intelligence will soon be captivating over trained manual work that now is mostly cared by humans. Machines can carry out human-like tasks by new inputs as artificial intelligence makes it possible for machines to learn from experience. AI data from web of science database from 2008 to 2017 have been mapped to depict the average growth rate, relative growth rate, contribution made by authors in the view of research productivity, authorship pattern and collaboration of AI literature. The Lotka's law on authorship productivity of AI literature has been tested to confirm the applicability of the law to the present data set. A K-S test was applied to measure the degree of agreement between the distribution of the observed set of data against the inverse general power relationship and the theoretical value of α =2. It is found that the inverse square law of Lotka follow as such.

Santiago Estrada, Sailesh Conjeti, Muneer Ahmad et al.

Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation. In this paper, we propose Competitive Dense Fully Convolutional Networks (CDFNet) by introducing competitive maxout activations in place of naive feature concatenation for inducing competition amongst layers. Within CDFNet, we propose two architectural contributions, namely competitive dense block (CDB) and competitive unpooling block (CUB) to induce competition at local and global scales for short and long-range skip connections respectively. This extension is demonstrated to boost learning of specialized sub-networks targeted at segmenting specific anatomies, which in turn eases the training of complex tasks. We present the proof-of-concept on the challenging task of whole body segmentation in the publicly available VISCERAL benchmark and demonstrate improved performance over multiple learning and registration based state-of-the-art methods.