Browsing by Author "Pandey, Bishwajeet"

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Energy Efficient Instruction Register for Green Communication
(International Journal of Engineering and Advanced Technology, 2019-01) Siddiquee, Shah Md Tanvir; Kumar, Keshav; Pandey, Bishwajeet; Kumar, Abhishek
Our work represents the interfacing of instruction register with FPGA. In this work we have taken three different FPGA of Virtex family that are Virtex 4, Virtex 5 and Virtex 6 and have observed the power variation of instruction register with this three FPGA. This experiment is done on a Xilinx 14.1 ISE design suite. And the power of instruction register with three FPGA is analyzed with an X Power tool. All the other chips power which is implanted on instruction register counts zero in total, dynamic and quiescent power consumption. In this experiment, only one LUT flip flop pair is used. On comparing the power of instruction register with the three FPGA of Virtex family, we concluded that 90 nm Virtex-4 FPGA requires the least power among all the three FPGA.
Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach
(2024-08-15) Hossain Tuhin, Hemayet; Abdul Kayum, Md; Sarker, Md Rahmatul Kabir Rasel; Siddiquee, Shah Md Tanvir; Pandey, Bishwajeet
Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the ’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry.
Recognizing Language and Emotional Tone from Music Lyrics Using IBM Watson Tone Analyzer
(Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2023, IEEE, 2019-10-17) Marouf, Ahmed Al; Hossain, Rafayet; Sarker, Md. Rahmatul Kabir Rasel; Pandey, Bishwajeet; Siddiquee, Shah Md. Tanvir
Music has a soothing impact on listener's mood and emotional states. Apart from the rhythm, sequence, instrumental effects on a song, lyrics could be considered as the most vital element. Lyricists' mood and affection towards a song while writing could be understand from the lyrics. Lyrics does have the elements of fictions such as language tone, language style, diction and voice are well maintained in music lyrics. Understanding the tone of a song both language and emotional tones are essential to develop different interactive applications. Music players, video repositories, video sharing sites could use the understandings to recommend next song to play according to the music interest or mood of the listeners. In this paper, we have investigated the possibilities to use IBM Watson Tone Analyzer, an API service to analyze language and emotional tones from song lyrics. We have extracted the features from a 300 English song dataset using the supported API service and formulated a machine learning methodology to classify the language tone (analytical, confident and tentative) and emotional tone (anger, fear, joy and sadness). For classification, we have applied different classifiers including Naïve Bayes, decision tree, random forest, sequential minimal optimization and simple logistic regression.
Recommendation Approach of English Songs Title Based on Latent Dirichlet Allocation Applied on Lyrics
(Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019, IEEE, 2019-10-17) Hossain, Rafayet; Sarker, Md. Rahmatul Kabir Rasel; Mimo, Mehejabin; Marouf, Ahmed Al; Pandey, Bishwajeet
The significance of music has evolved due to the vast diversity of entertainment industry. Songs are the widely used entertainment segment that can influence directly to the heart of the listeners. Choosing a suitable title for a song is considered as a common problem faced by the music directors. As the title gives the first impression of the song and only by the title listeners usually decide whether they will listen to this song or not, thus makes it a challenging task to determine. Lyrics are the most influential part of a particular song apart from the tune, rhythm, fusion, singer, genre etc. In this paper, we propose an approach to estimate and recommend the title of the song based on its lyrics. We have applied Latent Dirichlet Allocation (LDA) to find the hidden or implied topic of the song. The output of the LDA algorithm provides scoring on the significant words, which are passed to an estimation process to generate a song title. The proposed approach was experimented on over 200 English songs database having vast diversity in genre. The approach could be evaluated by the existing song title and the evaluation process is same as any recommendation system.